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Image steganography techniques for resisting statistical steganalysis attacks: A systematic literature review
Image steganography techniques for resisting statistical steganalysis attacks: A systematic literature review
https://orcid.org/0000-0002-5621-1435
Apau Richard Conceptualization Data curation Investigation Methodology Writing – original draft *
Asante Michael Software Supervision Validation Writing – review & editing
Twum Frimpong Supervision Validation Visualization Writing – review & editing
Ben Hayfron-Acquah James Data curation Supervision Validation Writing – review & editing
Peasah Kwame Ofosuhene Conceptualization Validation Writing – review & editing
Department of Computer Science, Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
Solak Serdar Editor
Kocaeli University, TÜRKIYE
Competing Interests: The authors have declared that no competing interests exist

* E-mail: rich4u34@yahoo.com
16 9 2024
2024
19 9 e030880726 4 2024
25 7 2024
© 2024 Apau et al
2024
Apau et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Information hiding in images has gained popularity. As image steganography gains relevance, techniques for detecting hidden messages have emerged. Statistical steganalysis mechanisms detect the presence of hidden secret messages in images, rendering images a prime target for cyber-attacks. Also, studies examining image steganography techniques are limited. This paper aims to fill the existing gap in extant literature on image steganography schemes capable of resisting statistical steganalysis attacks, by providing a comprehensive systematic literature review. This will ensure image steganography researchers and data protection practitioners are updated on current trends in information security assurance mechanisms. The study sampled 125 articles from ACM Digital Library, IEEE Explore, Science Direct, and Wiley. Using PRISMA, articles were synthesized and analyzed using quantitative and qualitative methods. A comprehensive discussion on image steganography techniques in terms of their robustness against well-known universal statistical steganalysis attacks including Regular-Singular (RS) and Chi-Square (X2) are provided. Trends in publication, techniques and methods, performance evaluation metrics, and security impacts were discussed. Extensive comparisons were drawn among existing techniques to evaluate their merits and limitations. It was observed that Generative Adversarial Networks dominate image steganography techniques and have become the preferred method by scholars within the domain. Artificial intelligence-powered algorithms including Machine Learning, Deep Learning, Convolutional Neural Networks, and Genetic Algorithms are recently dominating image steganography research as they enhance security. The implication is that previously preferred traditional techniques such as LSB algorithms are receiving less attention. Future Research may consider emerging technologies like blockchain technology, artificial neural networks, and biometric and facial recognition technologies to improve the robustness and security capabilities of image steganography applications.

The author(s) received no specific funding for this work. Data AvailabilityAll relevant data are within the manuscript and its Supporting Information files.
Data Availability

All relevant data are within the manuscript and its Supporting Information files.
==== Body
pmc1. Introduction

Information technology has revolutionized many aspects of the human society. Presently, computing technologies have permeated our daily activities including shopping, banking, education, and communication [1]. These technologies have boosted productivity and automated many tasks. With the increased pervasive network connectivity and technology convergence, an enormous amount of information is produced, processed, stored, and shared every day [2]. For example, Facebook sees over 147, 000 pictures uploaded every 60 seconds [3]. Organizations rely heavily on information technologies for communication and information sharing [4]. Technological platforms such as email, videoconferencing, and social media apps are widely used by organizations to facilitate employee information sharing, meetings, and/or public product advertising.

While information sharing through computing technologies has its benefits, it is also susceptible to various threats such as cyber-attacks, data theft, and data breaches [5]. Numerous reports exist regarding data leakage, data loss, and unauthorized access to confidential information in digital communication [6,7]. Data breaches have affected many companies and organizations across different sectors, resulting in multimillion-dollar losses to cyber criminals [8]. Cybercrime Ventures [9] estimated annual cost of data breaches to reach 10.5 trillion United States dollars globally by 2025. Records totaling 4.5 billion were exposed by mid-2018 alone, whereas in 2019 identity records totaling 2.7 billion were exposed [10]. For example, the Thales 2022 data threat report revealed that 45% of companies in the United States experienced data breaches [11]. Additionally, in 2022, T-Mobile data breach pay-outs to customers and regulation fines cost the company 350 million dollars [12]. An Analysis by Nallainathan [13] projected a rise in cyber-attack trends in the next decade. As organizations suffer these occurrences, they incur significant financial and reputational losses [13]. According to Bouveret [14], more than 1 billion US dollars has been lost by financial institutions since 2010. Further, the operations of many institutions are threatened by these threats as cyber-attacks continue to grow more complex and sophisticated. Poor security measures are at the heart of many of these data breaches. Consequently, securing communication and information exchange has thus become paramount.

Given the rapid pace of data compromises and the potential threats to the security of individual and organizational data, steganography, which is an information-hiding technique, and cryptography, a data protection approach has gained notable attention in recent years. While cryptography ensures data confidentiality by altering the meaning of the message being transmitted, steganography conceals the existence and contents of secret information [15]. In other words, cryptographic techniques transform the message such that its original meaning is obscured from an unauthorized entity [16] and steganography covertly embeds the message within an innocent-looking cover (or media) [17]. Although cryptography is effective in securing communication channels, it is limited because the jumbled messages arouse suspicion in the minds of intruders, who potentially may destroy the message [18]. Hence, the intended recipient may not get access to the message. Also, a technique called cryptanalysis serves as a countermeasure against cryptography with the intended aim of revealing a secret message, thereby undermining the security, privacy, and secrecy of the message [19–26]. Steganography therefore provides another layer of security to enhance the protection of data against unauthorized access and use. Steganography is effective for ensuring confidentiality, integrity, and availability [27].

Steganographic applications are categorized into five types. These are image steganography, network protocol steganography, text steganography, video steganography, and audio steganography [1]. However, image steganography has gained the most popularity due to the degree of redundancy associated with images [28]. As image steganography continues to gain relevance as an effective approach in the field of information security, techniques for detecting hidden messages have emerged. Specifically, steganalysis is a technique that aims at uncovering and extracting hidden messages from a cover (or media) that is gaining prominence in the domain [18]. Statistical steganalysis mechanisms such as RS attacks detect the presence of LSB-based hidden secret messages [29]. These mechanisms have exposed image steganography, rendering images a prime target for cyber-attacks. Given the rapid advancement and increasing sophistication of information technologies, steganalysis techniques are expected to grow more powerfully [15]. For the image steganography technique to be efficient, resistance against universal steganalysis attacks is paramount. Consequently, more robust image steganography techniques capable of withstanding statistical steganalysis attacks are urgently needed. A comprehensive understanding of image steganography techniques for resisting statistical steganalysis is required to safeguard information against detection, alteration, and modification and to guarantee data protection assurances and enhanced information security.

Yet existing studies that examine image steganography techniques are limited, and relevant review studies fail to provide detailed empirical-based discussions on issues related to image steganography techniques. In other words, existing studies have not adopted a standardized methodology for reviewing the selected publications [30–33]. For instance, Bhattacharyya and Banerjee [30], Febryan et al., [31], and Shehab and Alhaddad [34] all conducted review studies that employed steganography techniques to hide data in image, audio, and video but none of these studies adopted an empirical approach or standardized method for selecting the studies, potentially introducing errors, omissions, and biases that hinder informed decision-making.

This empirical systematic literature review aims to fill the existing gap in the literature and provides a comprehensive literature review on image steganography schemes proposed to resist statistical steganalysis attacks. Systematic literature reviews on image steganography techniques are limited, and the existing review studies do not provide an adequate and comprehensive understanding of the phenomena. This paper provides a holistic overview of the field’s advancements, methodologies, challenges, and emerging trends in statistical steganalysis attacks. The major contribution of this paper is as follows:

A systematic literature review of image steganography techniques capable of resisting steganalysis attacks is presented. Research articles from four reputable electronic databases comprising ACM Digital Library, IEEE Explore, Science Direct, and Wiley are selected.

Comprehensive analysis using quantitative and qualitative methods and tools is conducted on the selected articles to develop patterns, trends, techniques, methods, and performance of existing image steganography applications using standard evaluation metrics. This is intended to help information security practitioners and data protection scholars to be abreast with existing data protection schemes and measures.

Extensive comparisons are drawn among existing techniques to evaluate their merits and limitations as well as their robustness against statistical steganalysis attacks.

Finally, based on the analysis and findings, future directions would be provided in the field of image steganography aimed at guiding researchers and scholars to set the direction on emerging technologies and approaches that could be adopted for future research to improve security within the image steganography domain.

The rest of the paper is structured as follows: Section 2 of the paper provides an overview of background literature on image steganography and statistical steganalysis attacks, as well as discussions on existing review works and their limitations. The review methodology using PRISMA as demonstrated in Fig 2 is presented in Section 3. In section 4, comprehensive results following the qualitative and quantitative analysis are elaborated including future scope and research directions, whereas section 5 discusses the results and presents implications for the study findings. Finally, section 6 provides key findings, conclusions, limitations, and recommendations for future research studies.

Background literature

2.1 Image steganography

Information hiding in images has gained popularity in recent times [35]. Images have become important carriers to hide secret messages without changing the visual features and/or properties. As a result, images have become popular and widely used for steganography due to the degree of redundancy associated with them [36]. All image file formats are suitable for image steganography. File format types including TIFF, JPEG, PNG, GIF, and BMP are all appropriate to use. [37]. It is worth noting that each image file format has its advantages and disadvantages when employed for steganography purposes. Given that pixel values are utilized for image steganography, variations in pixel intensities between the original cover image and stego-images are sometimes experienced. The intensity variation is nonetheless subtle such that the undetectability and imperceptibility to the human visual system is achieved [38,39].

The commonality of images for steganography has subjected images to several targeted cyber-attacks including visual and statistical steganalysis attacks [40]. These attacks possess the ability to unearth concealed messages within images using steganalysis algorithms. Statistical steganalysis capabilities aimed at revealing hidden data in images include detection, extraction, disabling, and destruction of hidden data [41]. Tools and techniques used for such capabilities include lossy compression, denoising, image enhancement techniques, image approximation techniques, and geometrical modification [35]. These tools and techniques expose the vulnerabilities of image steganography on the digital landscape, rendering images a prime target of cybercriminal activities.

Image steganography uses three main traditional approaches (i.e., spatial domain, transform domain, and adaptive domain) to embed data [42]. The spatial domain approach entails the direct embedding of secret messages into image pixel values. This approach encompasses numerous techniques including the least significant bit (LSB) insertion algorithm [43–45], quantization-based methods [46], histogram-based methods [47], prediction error [48], modulo operations [49], and many other variations. Spatial domain methods have the advantages of high visual quality with minimal distortion effects, and high embedding payload capacity [38]. However, the spatial domain is less robust, making it susceptible to various forms of manipulation and attacks [38].

Given the challenges associated with spatial domain approaches, transform domain techniques emerged as a compelling alternative for secret data embedding [50]. The transform domain utilizes frequency sub-band coefficients to insert the secret message bits [51,52]. Although the data embedding and extraction processes are intricate compared to the spatial domain, this approach bolsters system security [50]. This embedding technique possesses the capability to withstand data manipulation approaches such as cropping, scaling, compression, and rotation. Some existing transform domain algorithms include Discrete Cosine Transform (DCT) [51], Discrete Fourier Transform (DFT) [53], Integer Wavelet Transform (IWT) [54], and Discrete Wavelet Transform (DWT) [55] among others. This method offers competitive advantages over spatial domain approaches by enhancing the robustness of the steganographic applications. However, both spatial and transform domain approaches have limitations [56], particularly regarding the susceptibility of the cover image to data manipulation and modification. Notwithstanding these limitations, spatial domain methods such as LSB Insertion algorithm and Pixel Value Differencing (PVD) remain the most prevalent data embedding techniques for steganographic applications [57]. The spatial domain method alters the LSBs of the carrier image by directly replacing the LSBs of the original cover image with the secret message bits, while transform domain randomizes all the bits in the carrier image [58].

Considering the intricacies associated with spatial and transform domains, the adaptive domain method also known as the model-based method or masking has surfaced. This method employs dynamic techniques for pixel selection for data embedding and estimating an allowable number of bits that can be hidden within the carrier object [50]. Examples of this method include artificial intelligence, blockchain technology, machine learning, and genetic algorithms. Recent innovations have seen the implementation of biometric techniques and facial recognition technologies for image steganography, contributing to the security enhancement and robustness [59–63]. Adaptive techniques have a comparative advantage over spatial and transform domains due to their robustness and the ability to avoid detection by statistical steganalysis attacks. This method is also able to efficiently balance the tradeoffs between embedding capacity and security. The trade-off high embedding capacity on one side and security and robustness improvement on another side, remains a challenge in image steganography applications, for which constant innovations are required.

2.2 Statistical steganalysis attacks

Steganalysis techniques undermine the security capabilities of steganography, as they detect messages concealed in images to reveal the message and estimate the size/length. Given that image steganography has gained prominence for secret information hiding, image steganalysis emerges as a countermeasure. Image steganalysis exploits image processing techniques such as cropping, filtering, and blurring to detect, extract, disable, or destroy hidden information within cover objects [64]. Steganalysis algorithms are extant, some of which include pixel difference histogram (PDH) analysis, sample-pair analysis, RS analysis, and Chi-square (X2) analysis [58] among others. RS steganalysis can detect LSB-based substitution stego-images, whereas Chi-square analysis which is based on a statistical distribution of binary values (0s and 1s) can determine if the image intensities follow random or distributed patterns. Statistical steganalysis process extracts the statistical characteristics of an image to accurately detect and estimate the exact size of hidden messages within a stego image [65]. By so doing, the hidden information is unveiled, and their length estimated. This breaches the confidentiality requirement of data transmission. All types of steganalysis possess the capability to identify, detect, and extract secret information hidden within a carrier object. For instance, PDH analysis can analyze and detect PVD-based image steganography. The analysis focuses on searching for the algorithm employed for the secret message concealment.

Chi-Square (X2) statistical steganalysis was proposed by Westfeld and Pfitzmann [66] with the ability to detect sequentially embedded messages within an image. This approach, however, could not identify the presence of hidden messages based on random embedding. Notably, Provos [67] improved the technique proposed by Westfeld and Pfitzmann [61] to have the ability to detect and estimate both sequentially and randomly hidden messages. The sample-pair technique proposed by Dumitrescu et al., [68], is also another effective approach to detecting hidden messages based on LSB steganographic hiding process. Among the various types of statistical steganalysis, the RS attack developed by Fridrich et al. [69] is the most effective and well-known steganalysis technique which possess the capability to detect and reveal secret messages embedded within an image. RS steganalysis technique detects both sequential and random embedded secret messages. Statistical attack techniques adeptly differentiate stego-images containing secret messages from cover images. This is done by mathematically investigating the relationship that exists between adjacent pixel groups and the pixel values of the stego-image, and the cover image [70]. Following the earlier work by Fridrich et al. [69], several steganalysis techniques with improved performance and detection capabilities have emerged [65–69,71–77]. The growing sophistication, complexity, and accuracy performance of steganalysis techniques have meant that a more secure image steganography scheme is required.

2.3 Previous/Related works

Empirical studies providing systematic review on image steganography techniques and methods aimed at resisting statistical steganalysis attacks are limited. Existing studies have failed to provide detailed empirical-based discussions on issues related to image steganography techniques and lacked a standardized methodology for reviewing the selected publications/articles. Ashwin et al., [78] conducted a review of image steganography techniques as well as steganalysis techniques capable of detecting secret information hidden in images. The study identified research trends, challenges, methods, and techniques for image steganography. Although Ashwin et al., [78] study provided early perspectives to scholars on existing techniques for resisting steganalysis attacks, the study was limited to only two embedding process approaches (i.e., spatial and transform). The study failed to provide broader insights into other notable techniques and algorithms dominating the field. The study also failed to adopt a standardized methodology for conducting the literature review. Subhedar and Mankar [79] focused on the issues and challenges of image steganography. The study provided key insights on image steganography performance evaluation metrics and explored various challenges that confront image steganography whose data embedding processes are based on spatial and transform domains. The study identified steganalysis techniques as key issues affecting the efficiency of steganography and provided future research direction. This study was however not systematic, as methods for selecting literature were not defined. The study also failed to discuss how existing techniques have performed against universal statistical steganalysis such as RDH and RS attacks.

Kadhim et al., [80] provided a review of image steganography techniques. The study discussed performance evaluation metrics as well as future research trends in the field of image steganography. The study provided key insights to researchers on the trends of digital image steganography but failed to provide a broader and comprehensive systematic review of key algorithms dominating the field. Standard methods were not applied in the selection of literature for the survey review. Mandal et al., [81] provided a review of digital image steganography tools available for embedding secret messages. The survey provided some image steganography techniques including adaptive and deep learning techniques and offered some key examples of some popular steganography tools. Comparison of the various tools were provided. Challenges of deep learning-based steganography were also enumerated. The study failed to adopt a standardized methodology for conducting the literature review and did not provide a comprehensive insight into all existing image steganography techniques/approaches. The study was limited to spatial and transform domain methods. Perhaps, the most comprehensive study and closely related to this paper is a systematic literature review conducted by Kaur et al., [50]. Kaur et al., [50] adopted standardized systematic literature review guidelines and selected 61 pieces of literature from four key databases comprising Web of Science, IEEE, Wiley, and ACM. The studies selected were published from 2011 to 2022. The results of the study show that extensive milestones for image steganography techniques have been achieved. Progress in all three data embedding processes (ie spatial, transform, and adaptive approaches) has seen notable improvement. The study further revealed that future research could focus on enhancing and striking an adequate balance between embedding capacity and robustness.

Other existing reviews focused on some specific domains within image steganography, further limiting the scope of the application of techniques for resisting statistical steganalysis. For example, Hussain et al., [82] provided a review on image steganography focusing on spatial domain techniques. The study highlighted some novel spatial domain techniques for image steganography including challenges and trends. Girdhar and Kumar [83] also provided a review of steganography techniques based on 3D images. Various 3D domain techniques including topological, geographical, and representation domains were discussed and compared in terms of payload capacity, resistance to attacks, and reversibility. Meng et al., [84] reviewed deep learning algorithm-based image steganography techniques. Various deep-learning algorithms were surveyed and discussed. Deep-learning algorithms used for coverless information hiding, steganalysis attacks, and watermarking were extensively presented and discussed. Qin et al., [85] comprehensively reviewed coverless image steganography techniques. The review provided a framework description of methods and techniques for coverless image steganography, highlighted recent developments in the area, and concluded that coverless image steganography provides resistance against steganalysis attacks.

Also, Puteaux et al., [86] focused their survey on reversible image steganography techniques. Techniques and methods compared included pixel value differencing or histogram shifting, re-echoing-based steganography, public key cryptography-based methods, prediction-based methods, and image partition-based techniques. Aslam et al., [87] conducted a review LSB based image steganography techniques. The review sampled 20 research studies published from 2016 to 2020. The 20 articles were further scaled down to 17 for the review. 20 data sets were identified for the evaluation of image steganography techniques. All the domain-specific studies reviewed [82–86] could not be conveniently classified as a systematic literature review except Aslam et al., [87]. The studies failed the threshold for systematic literature review when compared to the guidelines provided by Kitchenham and Charters [88]. The methods adopted for the study selection including inclusion and exclusion criteria, datasets, databases, data extraction methods, and queries were not detailed.

The above review works discussed may not be exhaustive for review research on image steganography techniques capable of resisting statistical steganalysis. However, the extensive literature search conducted in the most relevant scientific databases and libraries provided little evidence of a systematic literature review for image steganography techniques. The identified knowledge gap and other germane issues are the focus of this review. This research, therefore, seeks to conduct investigations into the literature on image steganography techniques capable of resisting statistical steganalysis attacks. By so doing, the review brings to the fore relevant studies on image steganography methods for resisting statistical steganalysis to bridge and/or expose the knowledge gap.

3. Review methodology

This research adopted a standardized methodology and procedure for the systematic literature review. The aim was to meet the objectives set out for the review. The study relied on PRISMA guidelines and procedures for conducting a systematic literature review. Many scholars have recently utilized PRSIMA for systematic literature review studies within the information technology landscape and was considered an effective and exhaustive framework for conducting systematic review studies [50,89–91].

3.1 Research approach

The PRISMA guidelines were chosen to ensure the review process is transparent, clear, and credible [92]. The processes involved in PRISMA include defining the systematic scoping review, identifying potential studies through literature searches in relevant databases and electronic libraries using predefined keywords, abstract screening, selecting papers based on inclusion and exclusion criteria, article characterization, and mapping based on keywords and meta-analysis [93]. Based on the PRISMA guidelines, a data selection, extraction, and classification taxonomy were developed and implemented. The taxonomy defined review questions, literature search strategy, eligibility criteria for inclusion and exclusion, data analysis framework, and criteria for resolving opinion disparities among researchers.

3.2 Review research questions and protocol

Kitchenham and Charters [88] argued that review questions and review protocols are important components of the systematic literature review process as they reduce the researcher’s biases and provide a critical framework to guide acceptable systematic reviews. Review questions are formulated during the initial stages of study planning to situate the study goals as the foundation upon which the study hinges [93]. This study adopted the Goal-Question-Metric approach suggested by Caldiera and Rombach [94] (See Table 1 for the Goal-Metric Questions). This Goal-Question-Metric has previously been used by Lun et al., [95] and Wiafe et al., [96] as an efficient and effective approach for deriving systematic review objectives.

10.1371/journal.pone.0308807.t001 Table 1 Adopted Goal-Question-Metric [94].

The Purpose	The study analyses	
The Issue	Trends in publication, application areas, techniques, security impacts, and future scope and research direction	
The Object	Image steganography techniques for resisting statistical steganalysis attacks	
The Viewpoint	From 2012 to 2023	

Statistical steganalysis attacks are growing at a tremendous pace. As such, techniques and methods for steganography that could withstand such attacks have become topical. Questions such as the most used image steganography techniques for resisting steganalysis attacks, the performance and security impact of image steganography techniques, and future scope and research direction for techniques within the image steganography domain remain critical and unanswered concerns that require addressing. These knowledge gaps need to be addressed. The review questions, the reason behind the questions, and the research approach to achieve the questions are listed in Table 2.

10.1371/journal.pone.0308807.t002 Table 2 Formulated review questions and motivation.

Item	Research Questions (RQ)	Rationale	Research Approach	
RQ1	Q1. What have been the Trends in Publication of Image Steganography Applications?	This question aims to classify the reviewed studies including the publication outlets, country of origin of studies and yearly publication trends with the view of bridging the knowledge gap within the image steganography domain	Quantitative Approach	
RQ2	Q2. Which Methods and Techniques are Used in Image Steganography for Resisting Statistical Attacks?	This is aimed at identifying the various image steganography techniques and methods currently in use for resisting attacks. It would also provide analysis on the most dominant methods and classify them based on the embedding process.	Quantitative Approach	
RQ3	Q3. What are the Standard Performance Evaluation Metrics for Image Steganography Techniques	The motivation behind this question is to identify the current standard performance evaluation metrics that have been used to measure the performance of image steganography techniques. This is to provide researchers with the modern trends in existing image steganography technique evaluation	Qualitative Approach	
RQ4	Q4 What Security Impact Has the Techniques have on Image steganography for Resisting Statistical Attacks?	The rationale for posting this question is aimed at analysing and classifying the impact that the existing techniques and methods have had on resisting steganalysis attacks. This will allow researchers and data protection professionals to understand the advantages or strengths as well as the disadvantages or limitation of existing image steganography techniques and how best to bridge the gap	Qualitative Approach	
RQ5	Q5. What are the Future Scope and Research Direction for Image Steganography?	This question explores and identifies future possible research interest areas for scholars including new techniques and technologies that could be explores to enhance the attack resistant nature of image steganography. It also seeks to provide researchers with future aspirations on emerging areas of interest within the image steganography domain.	Qualitative Approach	

Following the formulation of the research questions and to further avoid biases in the literature search strategy, search terms and keywords, and study selection, the review protocol was separately developed by each of the members of the research team. The individual protocols were merged and further refined by the research team in a protocol development meeting. The merged protocol was refined, and the final protocol was adopted after an extensive review process and corrections. Fig 1 provides a detailed diagrammatic representation of the final protocol adopted for the study demonstrating the main review processes followed.

10.1371/journal.pone.0308807.g001 Fig 1 Adopted review protocol for methodological analysis.

3.3 Literature strategy

Brereton et al., [83] identified seven electronic databases as key for conducting exhaustive literature searches for studies within the information technology landscape and for software engineers specifically. These databases are IEEExplore, ACM Digital Library, Google Scholar, Citeseer Library, INSPEC, ScienceDirect, and EI Compendex. SCOPUS, Wiley Online, Web of Science (WOS), and Springer Link are also considered relevant electronic libraries [83]. Before the actual search, a preliminary search was conducted on Google Scholar, Citeseer, and SCOPUS to identify the most appropriate databases, search terms, and search period. Based on the preliminary search, four (4) databases (i.e., IEEE, ACM Digital, ScienceDirect, and Wiley Online) were chosen. These electronic databases and libraries were chosen because they had the most relevant published studies on image steganography techniques. The keywords and search terms used for the database searches were made up of two categories. The categories were Steganography and related words (steganography, image, image steganography) and Steganalysis and related words (Steganalysis, statistical steganalysis, RS steganalysis). The search phrases were developed by combining words from both categories using the “AND” Boolean Operator. After several searches in databases by the researchers, five search terms were perceived as appropriate based on the results from the preliminary search. These terms were (i) “Steganography” and “Steganalysis” (ii) “Image Steganography” and “Steganalysis” (iii) “Steganography” and “RS Steganalysis” (iv) “Image Steganography” and “Statistical Steganalysis” and (v) “Image” and “Statistical Steganalysis”. The search period was limited to 2012 to 2023 inclusive.

3.4 Eligibility criteria

For a publication to form part of this review, clear inclusion and exclusion criteria were defined. To be included, publications should have been written in English. Also, publications should have discussed image steganography and/or steganalysis attacks performance evaluation metrics. That is, publications whose titles related to image steganography and/or steganalysis attacks were included. Further, papers published from 2012 to 2023 were considered. Apart from these, only peer-reviewed publications were accepted. For the exclusion criteria, non-empirical studies were rejected. This suggests that point-of-view papers, review papers, and reports were excluded. Also, only peer-reviewed journal and conference papers were included. Book sections, chapters, posters, and thesis were excluded from the review. Moreover, publication abstracts that showed no relationship with the search terms were excluded. Publications whose content did not discuss how image steganographic techniques are employed to resist steganalysis attacks were removed. Lastly, publications ranked as low quality as agreed by the review team were excluded.

3.5 Study selection

Based on the search criteria, two (2) members of the review team performed independent searches using the identified search terms on all four (4) databases. For all searches, the search period was limited to 2012 to 2023 inclusive. The two (2) independent results were merged into one dataset. A total of 5146 publications were compiled. The dataset (n = 5146) was then screened to remove duplicates. After the duplicates were removed, 1379 publications remained. Next, the titles of the publications were scanned to determine their relatedness to the objectives of this review. For example, studies whose titles did not suggest any relation to image steganography techniques were removed. Next, the dataset was examined to maintain only journal and conference papers. Book sections, chapters, posters, and thesis were removed. Further, all non-empirical papers were discarded. This process reduced the total number of publications to 902. Reports were sought for retrieval and 13 reports were not retrieved. A total number of 889 records were maintained. After assessing the papers for eligibility, 736 papers were removed.

Two (2) members of the review team separately read the abstracts of the remaining publications (n = 153) to determine their relatedness to the search terms. The separate reports from the two (2) members were discussed by all members of the review team and merged. In cases of any disparities, a vote was conducted to resolve the issue. This activity further reduced the number of publications to 136. Lastly, two (2) other members of the review team read the content of the 136 publications to assess their quality. Their reports were also discussed and debated. Based on these discussions, 125 publications were retained as appropriate for review. Fig 2 provides a detailed summary of the selection process for the identified publications. Thus, 125 papers remained as final papers included in the systematic literature review. Also, a summary of the number of papers selected from the various electronic databases and the search terms is shown in Table 3.

10.1371/journal.pone.0308807.g002 Fig 2 PRISMA flow diagram for publication selection process.

10.1371/journal.pone.0308807.t003 Table 3 Detailed record of articles selected for the systematic literature review.

Electronic Database /Library	Shortlisting	Steganography AND Steganalysis	Image Steganography AND Steganalysis	Steganography AND RS Steganalysis	Image Steganography AND Statistical Steganalysis	Image AND Statistical Steganalysis	Total	
ACM	Retrieved Articles	454	135	122	302	14	1027	
Selected	3	2	1	4	1	11	
Rejected	451	133	121	298	13	1016	
IEEE	Retrieved Articles	590	971	415	302	72	2350	
Selected	15	29	13	7	2	66	
Rejected	575	942	402	295	70	2284	
ScienceDirect	Retrieved Articles	321	103	32	753	45	1254	
Selected	15	6	1	21	3	46	
Rejected	306	97	31	732	42	1208	
Wiley	Retrieved Articles	125	190	116	51	33	515	
Selected	0	1	0	1	0	2	
Rejected	125	189	116	50	33	513	

4. Results and analysis

4.1 Publication trends

The selected publications were analyzed to understand the publication trends. The information recorded for this analysis included the year of publication, publication outlet, publication type, geographic origination of corresponding authors, and number of citations. The results show that publications on image steganography techniques for controlling statistical steganalysis attacks have increased considerably. For the year of publications, the results show fluctuations in the number of publications per year from 2012 to 2017 (see Fig 3). Since 2017, the number of publications per year increased tremendously. Articles published from 2018 to 2023 represented 73% of the total number of publications reviewed. This suggests a growing interest in image steganography studies for combatting steganalysis attacks. The analysis also shows an interesting result for the post-coronavirus Pandemic era (COVID-19), as approximately 49% of all articles were published from 2021 to 2023. This shows tremendous development of techniques against statistical attacks, following the numerous cyber-attacks, data breaches, and data compromises that were experienced during the peak of the COVID-19 lockdowns and global work-from-home phenomenon.

10.1371/journal.pone.0308807.g003 Fig 3 Yearly publication trends of reviewed studies.

The results also indicated a skewed interest in publishing outlets. From the total of 125 papers reviewed, 66 (53%) were published with IEEE and 46 (37%) by ScienceDirect. Fig 4 indicates the breakdown of the trend by publication outlet. Further, the analysis of the publication types revealed most of the reviewed publications were journals (57%) (n = 125).

10.1371/journal.pone.0308807.g004 Fig 4 Publication trend by publication outlet.

Similarly, the results were geographically skewed. The affiliations of the corresponding authors at the time of publication were used to extract the geographic originations of the papers. The majority (86%) of the reviewed papers (n = 125) originated from Asia followed by Europe (8%). India (43 of 125) and China (37 of 125) recorded the highest number of publications respectively. Fig 5 shows a summary of the geographical locations of all corresponding authors for the selected papers used for analysis.

10.1371/journal.pone.0308807.g005 Fig 5 Publication trend by geographic location.

The number of citations per paper at the time of this review was also analyzed. Majority (107 of 125) of the papers had 50 or lesser citations and only 8 had 100 or more. S1 Appendix shows the detailed list of the reviewed studies.

4.2 Image steganography techniques and methods

The review analyzed the methods and techniques that have been utilized in image steganography to resist statistical steganalysis attacks. Over 57 image steganography techniques and methods were identified. However, the techniques that have dominated image steganography studies are Modified LSB (M-LSB), LSB Matching (LSB-M), PVD, Genetic Algorithm (GA), GAN, CNN, DL Neural Networks, Hamiltonian Path (HP), Adaptive Edge Detection (AED), RDH, Residue Number System (RNS), DCT, IWT, among many others have been identified in literature as improving the imperceptibility of image steganography. Some of these methods have been implemented alone or sometimes with a combination of two of the methods enumerated. Others combined the methods with LSB and cryptographic protocols such as AES, RSA, and Elliptic Curve Cryptography (ECC) for encryption and decryption to enhance data security. As a result, many combinations of the above-mentioned techniques exist. The techniques and methods showed the capacity to enhance the visual quality of the carrier image and proved to be secure against statistical steganalysis attacks.

Fig 6 shows that GAN (17) is the most adopted technique. This is followed by AED (14). A total of 20 studies implemented a version of LSB comprising M-LSB (4), LSB-M (10), and LSB plus others (6). GA, RDH, and PVD were each implemented in 9 studies. The techniques that were used by less than two publications were grouped as “Others”. Fig 6 gives details of the number of times other methods were utilized. Table 4 also gives a breakdown detail of the trend in publication year and techniques implemented. As already mentioned, the embedding process for image steganography techniques can be classified into three domains ie (i) Spatial Domain-Based Techniques, (ii) Transform Domain-Based Techniques, and (iii) Adaptive Domain-Based Techniques. The review results reveal that spatial domain-based image steganography techniques have attracted more attention, as approximately 43% of all the reviewed papers utilized spatial domain for the secret data embedding process. This is followed by adaptive techniques, where 38% of reviewed papers employed such techniques. The rest of the studies used transform domain image steganography techniques (19%) (See Table 4). Further analysis of the review was conducted to understand the application of the image steganography techniques and the primary embedding domain employed for data hiding. This was necessary to observe the trend of specific techniques within each domain of application.

10.1371/journal.pone.0308807.g006 Fig 6 Image steganography techniques and methods for resisting attacks.

10.1371/journal.pone.0308807.t004 Table 4 Publication and image steganography embedding domains (2012 to 2023).

	Spatial Domain- Based Techniques	Transform Domain-Based Techniques	Adaptive Domain- Based Techniques	
2012	4	3		
2013	2	1	2	
2014	4	2	1	
2015	4		1	
2016	1	1	6	
2017	2			
2018	6	3	3	
2019	4	2	4	
2020	5		3	
2021	4	4	8	
2022	15	7	14	
2023	2	1	6	
Total	53	24	48	

The results as presented in Table 5 show that the spatial domain was the primary data embedding process for M-LSB, LSB-M, PVD, HP, LSB+Others, and AED. Also, almost all papers whose techniques were based on GA, GAN, DL, and CNN utilized the adaptive domain as the primary process of data embedding. Similarly, for DCT and IWT techniques, the transform domain method was mainly used. For RNS and RDH techniques, the domain for data embedding process was varied, whereas most of the other studies employed spatial domain and adaptive domain for the embedding process. The implication is that the spatial domain has gained wide application in use for image steganography, perhaps due to its advantage of high embedding payload capacity. Table 6 shows the trends in the year of publication versus image steganography techniques.

10.1371/journal.pone.0308807.t005 Table 5 Embedding domains verses image steganography techniques.

	Spatial Domain- Based Techniques	Transform Domain-Based Techniques	Adaptive Domain- Based Techniques	
M-LSB	10			
LSB-M	4			
LSB+OTHERS	6			
PVD	9			
GA			9	
DL			3	
CNN			11	
GAN			17	
AED	14			
RDH	2	5	2	
DCT		10		
IWT		7		
RNS	1	2	2	
HP	2			
OTHERS	5		4	
Total	53	24	48	

10.1371/journal.pone.0308807.t006 Table 6 Image steganography techniques for resisting steganalysis attacks (2012 to 2023).

	M-LSB	LSB-M	LSB + Others	PVD	GA	DL	CNN	GAN	AED	RDH	DCT	IWT	RNS	HP	Others	
2012	1			2						1	3					
2013									1	1	1			1	1	
2014		1			1				1		1	1			2	
2015	1		1	1	1				1							
2016	1				2		2		1	1					1	
2017	1												1			
2018	2			1	1		1	2	2		1	1		1		
2019		1				1	2	1	3			1	1			
2020	2			1			1	1	2	1						
2021			3	1	1	1	1	4			2	2	1			
2022	2	2	1	3	1	1	3	7	1	5	1	2	2		5	
2023			1		2		1	2	2		1					
Total	10	4	6	9	9	3	11	17	14	9	10	7	5	2	9	

4.3 Performance evaluation metrics for image steganography techniques

The implementation of image steganography is aimed at achieving some key objectives. The key objective parameters are high embedding payload capacity, imperceptibility (visual quality of resulting stego-image), robustness (distortion resistance), and security (un-detectability) among others. However, there is a trade-off between the performance evaluation parameters as most of the parameters result in opposite impacts with each other. For instance, techniques proposed to achieve high hiding capacity result in image distortion that ultimately reduces security and data protection. To achieve the objectives of image steganography techniques, various evaluation metrics are utilized. To measure imperceptibility, many studies have used Mean Square Error (MSE) [97], Peak-Signal-to-Noise-Ratio (PSNR) [98,99], Segmented Signal-to-Noise-Ratio (SNRseg) [100] and/or Signal-to-Noise-Ration (SNR) [101]. Also, Pearson Correlation Coefficient (NC) [102], Correlation Factor (r) [103], and Structural Similarity Index Measure (SSIM) [104,105] are used to measure the similarity between the cover image and the stego image to determine the image quality matrix. Bit Error Rate (BER) [106] is often used to measure the image distortion resistance, whereas Regular-Singular (RS) analysis [107,108] has proven effective in analyzing the detectability of the image steganography techniques against steganalysis attacks. Given that high embedding capacity is a key evaluation metric for image steganography techniques, Bits Per Pixel (BPP) is often used [109]. The dominant performance evaluation metrics for the reviewed papers, are PSNR, MSE, NC, SSIM, BPP, and RS analysis. The most used evaluation metrics are discussed below. However, the performance metrics used by each reviewed paper will be reported to ensure standardization and quality metrics comparison.

Imperceptibility is an important criterion in steganography [50]. Distortions between the original cover image (CI) and the resulting stego image (SI) must be relatively low to ensure higher imperceptibility of the image against attacks. Image Quality Measurement (IQM) is a mathematical approach to determining the quality of SI. When a secret message is embedded in the original selected CI, changes are noticed in the pixel values of the CI. Such changes affect the quality of the resulting SI. It is important to measure the changes in pixel values to ensure the SI is imperceptible. PSNR measures the distortion between CI and resulting SI. PSNR is determined using Eq 1 written as [98]: PSNR=20⋅log10(MAXI)−10⋅log10(MSE) (1)

MAXI. represents maximum oixel value, whereas the MSE is Mean Square Error. The MSE measures of noticeable distortion between CI and SI. MSE is determined using Eq 2 [97]: MSE=1mn∑i=0m−1∑j=0n−1[I(i,j)−K(i,j)]2 (2)

M and N represent the image height and width respectively. The lower the values obtained for MSE, the less distorted the difference between the CI and SI. Also, the higher the PSNR value, the higher the visual quality, thus higher imperceptibility.

Robustness of image the steganography technique proves that it is distortion resistant. To ensure that the technique is resistant to distortion, the similarity between the CI and SI is checked to determine whether the image has been distorted after embedding the secret message. SSIM is an important metric to check the structural similarity between the original CI and the resulting SI. The SSIM metric is calculated using Eq 3, and written as [103]: SSIM(x,y)=(2μxμy+c1)(2σxy+c2)(μx2+μy2+c1)(σx2+σy2+c2) (3)

Where c1 = (k1, L)2 and c2 = (k2, L)2. μx and μy are the CI and SI mean intensity. The variances of x and y are represented ð2x and ð2y respectively, whereas ðxy represents the covariance of x and y. the pixel values varying range is denoted by L, and the constant parameters are represented by c1 and c2. k1 and k2 values are always to taken to be 0.01 and 0.03 respectively. The NC also checks the distortion resistance between CI and SI. NC computes the degree correlation between the CI and SI, is determined using mathematical Eq 4 as [102]: NC=∑M∑N(XMN−X¯)(YNN−Y¯)∑M∑N(XMN−X¯)2∑M∑N(YNN−Y¯)2 (4)

Where X is the CI, Y is the SI, Ẋ is the mean pixel intensity values for the CI, and Ȳ is the mean pixel intensity values for the SI. Fundamentally, the image steganography technique aims to avoid statistical steganalysis attacks. As a result, one key parameter in the design is undetectability. Steganalysis attacks can have access to the data in transmission, thereby breaking the data confidentiality parameter. As already mentioned, Regular-Singular (RS) attacks are some of the well-known attacks. RS analysis is therefore performed to ensure the technique developed can resist statistical attacks. RS analysis is defined over three kinds of block flipping. The block flipping are positive flippings (F1), negative flippings (F-1), and Zero (0) flippings (F0). F1, F-1, and F0 become flipping functions and form what is termed a flipped group. The flipped group results from applying the flipping functions on each divided image block pixel value. Eq 5 is for determining the flipped group function [70]. F(G)=(FM(1)(X1),FM(2)(X2),…,FM(n)(Xn)) (5)

Where M = M (1), M (2), …, M (n) represents the flipped mask, and M (i) has values indicating either 1, 0, or -1. G is regular if f (G) < f (F(G)) otherwise G is singular when f (G)>f (F(G)). The implementation requires first dividing the image into non-overlapping blocks and re-arranging each one of them into a vector G = (X1, X2, X3, …Xn). The blocks are arranged in a zigzag scan order. The discrimination function of the pixel’s correlation is measured using Eq 6 [70]: f(x1,x2,…,xn)=∑i=1n−1|xi−xi+1| (6)

The pixel values are represented by x and n is used to represent the number of pixels Also, f represents partial correlation between the adjacent pixels. A smaller f value means a stronger correlation exists between adjacent pixel values. Payload capacity is an important measure for image steganography techniques. An algorithm for image steganography should be able to embed maximum secret messages without noticeable distortion. The overall effect, embedding the maximum payload capacity within the pixel values of the selected CI must be possible without distorting the visual quality of the resulting SI. Basically, the number of secret bits that have been hidden in the CI is the embedding payload capacity, which is calculated using BPP as shown in Eq 7 and written as [108]: bpp=EmbeddingCapacityM×N (7)

Where M and N are the CI cardinality, and embedding capacity (EC) which refers to the number of secret bits that can be embedded within total CI pixel values is determined using Eq 8 [70]: EmbeddingCapacity(EC)=NumberofBitsUsedtoHideDataTotalNumberofBitsinImage×100%. (8)

4.4 Performance metrics analysis

The performance evaluation metrics for all 125 reviewed papers are provided. The analysis covers the techniques employed, strengths, limitations, and results obtained in each reviewed paper. The problems or issues often discussed in image steganography research are diverse. Concerns such as the tradeoffs between embedding capacity and security, statistical attacks against image steganography systems, stego image distortion, low embedding capacity, and low visual image quality of stego images remain some key challenges and issues that are generally raised and discussed within the image steganography domain. As a result, most techniques are proposed to address these challenges. The analysis also covers the issues and problems discussed by the various articles that warranted the proposed techniques and methods. The reviewed papers are grouped according to the primary embedding process adopted. Table 7 covers papers based on Spatial Domain-Based Techniques, Table 8 covers papers based on Transform Domain-Based Techniques, and Table 9 is based on Adaptive Domain-Based Techniques. The evaluation metric indicated in each reviewed paper is reported.

10.1371/journal.pone.0308807.t007 Table 7 Spatial domain-based image steganography techniques.

Reviewed Study (RS)	Year	Problem/Issue	Technique/ Method	Strength	Limitation	Evaluation Metric Results	
RS43	2012	Low visual quality	PVD, HVS and diamond Encoding (DE)	Improvement in visual image quality	The payload capacity is low	BPP = 1.000
PSNR = 37.66	
RS44	2012	Trade-off between Security and Capacity	PVD	Successful secret image imperceptibility and high quality stego image	Payload estimation not offered	PSNR = 41.58
RS = 2.4%	
RS66	2012	Statistical Steganalysis Attacks	RDH and LSB	Capable of resisting both RS and Chi-Square attacks	Embedding capacity relatively low	Capacity = 90%
PSNR = 50.51
RS = 6%	
RS101	2012	Statistical Steganalysis Attacks	M-LSB	High visual stego image quality	Cannot withstand complex RS steganalysis	Capacity = 497,849
PSNR = 31.69	
RS42	2013	Low visual quality	HP and LSB	The technique produces minimum distortion on stego-image	Low embedding payload capacity	BPP = 1.000
PSNR = 52.52
MSE = 0.3640	
RS52	2013	Low visual quality	AED and LSB-M	Robust against some known steganlaysis attacks	Low embedding capacity detected	Capacity = 10%
RS = 1.5%	
RS53	2014	Low embedding capacity	AED and LSB	Good stego image quality	Noticeable image distortion with high payload	Capacity bits = 12929
PSNR = 40.79	
RS54	2014	Stego Image Distortion	AED and LSB	Provided better security and minimised distortion	Very low payload capacity	BPP = 0.5000
RS = 0.17
	
RS97	2014	Trade-off between Security and Capacity	LSB-M	High quality visual image quality	Performance metrics extremely low below threshold	0.2031
PSNR = 11.96	
RS45	2015	Statistical Steganalysis Attacks	PVD and Patched Reference Table (PRT)	Difficult to detect by RS schemes	Noticeable distortion with high embedding rate	RS = 0.600
BPP = 0.800	
RS55	2015	Trade-off between Security and Capacity	AED, LSB, Chaotic, and GA	Adequate balance between payload capacity and security	Realtime efficiency of algorithm is slow	BPP = 4.000
PSNR = 40.95
MSE0.3421
NC = 0.9048
SSIM = 0.9887	
RS102	2015	Trade-off between Security and Capacity	M-LSB	High visual quality and better payload capacity	Algorithm execution time is high	Capacity = 262000
PSNR = 56.44
RS = 0.4345	
RS112	2015	Statistical Steganalysis Attacks	LSB and Adaptive Key Technique	Ability to withstand steganalysis attacks and good embedding capacity	High values for computational complexity	BPP = 3.000
PSNR = 64.15
MSE = 0.2500	
RS56	2016	Stego Image Distortion	AED, LSB and Symmetric Encryption	Produced imperceptible SI with minimal embedding distortion	High computational complexity	BPP = 3.000
MSE = 0.594
PSNR = 50.39	
RS103	2016	Statistical Steganalysis Attacks	M-LSB and RSA	Very high SI quality and high imperceptibility	Payload is very low	PSNR = 74.02	
RS75	2017	Statistical Steganalysis Attacks	RNS, Encryption and LSB	Robustness against statistical steganalysis attacks	Noticeable distortion with increased payload	Capacity bit = 131072
PSNR = 51.93
MSE = 0.4169
RS = 0.350	
RS104	2017	Statistical Steganalysis Attacks	M-LSB and Contrast Stretching	Robust against RS attacks	Payload capacity is relatively low	Capacity = 30%
RS = 0.0564
PSNR = 54.08
MSE = 0.0374
	
RS41	2018	Trade-off between Security and Capacity	HP and LSB	Achieved increased payload and high imperceptibility	Some complex known RS attacks can detect secret message	BPP = 3.000
PSNR = 39.39
NC = 0.9991
SSIM = 0.9870	
RS46	2018	Trade-off between Security and Capacity	PVD, LSB and AES	Robustness against attacks	Improvement of algorithm efficiency required	BPP = 4.000
PSNR = 36.38
SSIM = 0.9403
NC = 0.1465
RS = 0.35	
RS57	2018	Trade-off between Security and Capacity	AED, LSB and dilation operator	Improved embedding capacity with high imperceptibility and robustness	Low embedding capacity	BPP = 1.236
PSNR = 43.62
MSE = 2.824
SSIM = 0.9980
	
RS58	2018	Stego Image Distortion	AED and LSB	Robustness and high visual stego image quality	Low embedding capacity	BPP = 0.300
PSNR = 57.33
RS = 0.0350	
RS105	2018	Low visual quality	M-LSB and Chaotic map	The application proved immune against visual degradation	The capacity is low	BPP = 0.900
PSNR = 44.09
SSIM = 0.9700	
RS106	2018	Statistical Steganalysis Attacks	M-LSB	Stego image have low probability of detection	Distortion noticeable with increased capacity	PSNR = 48.24
SSIM = 0.9935
RS = 0.4000	
RS59	2019	Statistical Steganalysis Attacks	AED, PVD and LSB	Resists various known steganalysis attacks and provide better visual quality	High estimated embedding time	Capacity bit = 105432
PSNR = 35.68	
RS60	2019	Low visual quality	AED and LSB	High imperceptibility and SI visual quality	The embedding time estimation is longer comparatively	Capacity bit = 183500
PSNR = 48.59
MSE = 0.8990
SSIM = 0.9982
NC = 0.1763	
RS62	2019	Low visual quality, Stego Image Distortion, and Statistical Steganalysis Attacks	AED	Stronger statistical security and better image visual quality	Low embedding payload	Capacity bit = 1000	
RS98	2019	Low visual quality, and Statistical Steganalysis Attacks	LSB-M and Image Enlargement	High capacity with preserved image quality	Time complexity is high	BPP = 4.000
PSNR = 49.40	
RS47	2020	Statistical Steganalysis Attacks	PVD, LSB and DE	Better image quality and robust against attacks	Embedding capacity results not presented	PSNR = 47.99
SSIM = 0.9883	
RS49	2020	Statistical Steganalysis Attacks	PVD, LSB and DL	High accuracy estimation rate	Distortion noticed with increased payload	BPP = 2.000	
RS63	2020	Trade-off between Security and Capacity	AED	The average execution time is very efficient	Embedding capacity is relatively low	BPP = 0.6500
PSNR = 48.61
MSE = 1.256
SSIM = 0.9986	
RS69	2020	Low visual quality	RDH, IWT and AES	Accurate reconstruction of reference image	Higher time complexity	Capacity = 100%
PSNR = 31.99
SSIM = 0.9323
RS = 0.9843	
RS107	2020	Trade-off between Security and Capacity	M-LSB and Pseudo Random Number Generator (PRNG)	Robustness against statistical steganalysis and increased capacity	The time complexity for the algorithm is high	BPP = 3.000
PSNR = 89.03
MSE = 0.0001	
RS108	2020	Statistical Steganalysis Attacks	M-LSB and PRNG	High imperceptibility and robustness	Embedding capacity not discussed	PSNR = 83.27
MSE = 0.0003
SSIM = 0.9999	
RS48	2021	Trade-off between Security and Capacity	PVD and LSB	Super high embedding rate capacity	Imperceptibility performance below threshold	BPP = 8.88
PSNR = 25
SSIM = 0.9999
NC = 0.8710RP	
RS50	2021	Trade-off between Security and Capacity	PVD, IWT and LSB	Withstand some known steganalysis tools	Low stego visual image quality	BPP = 2.2800
PSNR = 33.83
SSIM = 0.9820
NC = 0.9970
RS = 0.1020	
RS113	2021	Statistical Steganalysis Attacks	LSB and AES	Enhanced security for secure data transmission	Performance metrics not discussed	N/A	
RS114	2021	Statistical Steganalysis Attacks	LSB, AES, and Pixel Locator Sequence	Resistance to attacks and highly robust	The technique is not space-efficient	PSNR = 48.35
MSE = 0.9518
RS = 0.0275	
RS115	2021	Low visual quality, and Statistical Steganalysis Attacks, low embedding capacity	LSB, Random Number Generator and Range Technique	High imperceptibility and better embedding payload capacity	Time complexity for the algorithm is high	BPP = 2.9529
PNSR = 49.56
MSE = 0.0564
NC = 0.8256	
RS65	2022	Low visual quality, and Stego Image Distortion	AED and LSB	High capacity for hiding data	Image distortion and susceptible to RS attacks	Capacity bits = 5000
PSNR = 46.89	
RS51	2022	Low visual quality, Statistical Steganalysis Attacks	PVD and LSB	Resistance to known RS steganalysis attacks	Imperceptibility and visual quality image improvement required	BPP = 3.180
PSNR = 39.09
MSE = 0.4562
SSIM = 0.9986	
RS71	2022	Low visual quality, Statistical Steganalysis Attacks	RDH	Resist histogram and RS steganalysis attacks	Low embedding payload capacity	BPP = 1.43
PSNR = 43.13	
RS72	2022	Low embedding capacity	RDH and Encryption	High embedding capacity and robustness against attacks	Higher time complexity	BPP = 3.83
NC = 0.9822	
RS99	2022	Statistical Steganalysis Attacks	LSB-M and RDH	Better image quality	Low hiding capacity	BPP = 1.000
PSNR = 51.14
SSIM = 0.9983
RS = 0.543	
RS100	2022	Low embedding capacity, Statistical Steganalysis Attacks	LSB-M, RDH and PVD	Robust against some known statistical steganalysis	The embedding capacity is relatively low	BPP = 1.000
PNSR = 51.16
SSIM = 0.9942
RS = 0.3562	
RS109	2022	Statistical Steganalysis Attacks	M-LSB	Showed capacity to resist steganalysis	Performance evaluation metrics not discussed	PSNR mentioned but record not stated	
RS116	2022	Statistical Steganalysis Attacks	LSB and DWT	Resistance to RS attacks and provided enhanced security	High time complexity and computational time	PSNR = 40.09
MSE = 0.2322
SSIM = 0.9988
RS = 0.2500	
RS121	2022	Stego image distortion	Digital Still Images	Provided higher resistance to detection	Low embedding capacity	BPP = 0.2900
PSNR = 45.05
NC = 0.9997	
RS122	2022	Statistical Steganalysis Attacks	Generic Steganography Algorithm (GSA)	Robust against steganalysis	Higher Computational Complexity	BPP = 3.100
PSNR = 69.45	
RS123	2022	Statistical Steganalysis Attacks	Uniform Payload Distribution (UPD)	Provides better distribution to better security	Embedding capacity is relatively low	BPP = 0.500
RS = 1.3151
	
RS124	2022	Stego image distortion	Chaotic Encrypted Dual Radial Harmonic Fourier Moments	High robustness against attacks	Embedding rate not discussed	PSNR = 30.30
MSE = 0.4432
SSIM = 0.9776	
RS125	2022	Statistical Steganalysis Attacks	Intra-block Modification Optimisation (IbMO)	Improves security performance of image steganography	Time complexity is extremely high	BPP = 0.5000
PSNR = 40.12	
RS61	2023	Low visual quality, Statistical Steganalysis Attacks	RDH and Fuzzy Edge Detection	Robust against universal well-known attacks	High embedding capacity	BPP = 2.000
PSNR = 51.68
SSIM = 0.9931
RS = 0.4500	
RS64	2023	Statistical Steganalysis Attacks	Hybrid Edge Detection	Better robustness and high security	Low embedding capacity	PSNR = 57
SSIM = 0.9999	
RS110	2023	Statistical Steganalysis Attacks	Adaptive Error Correction	Robustness against Lossy JPEG compression	Performance evaluation metrics not discussed	BPP = 1.15
RS = 0.345	
RS111	2023	Statistical Steganalysis Attacks	LSB, AES, and Blowfish	Robustness against statistical attack	Low embedding capacity	PSNR = 85.64
MSE = 0.0001	
RS118	2023	Statistical Steganalysis Attacks	Guassian Edge Detection	Relatively high visual quality	Improved payload	BPP = 3.1270
PSNR = 36.4478
MSE = 0.7891
SSIM = 0.9593	
RS119	2023	Stego image distortion	LSB, Huffman Code, Encryption (MLE)	Adequate balance between security and embedding capacity	High computational complexity	PSNR = 83.99
MSE = 0.05
SSIM = 0.9999	

10.1371/journal.pone.0308807.t008 Table 8 Transform domain-based image steganography techniques.

Reviewed Study (RP)	Year	Problem/Issue	Technique/ Method	Strength	Limitation	Evaluation Metric Results	
RS80	2012	Statistical Steganalysis Attacks	DCT and IWT	High visual quality of SI and robustness against attacks	Embedding capacity not discussed	PSNR = 58.95
SSIM = 0.9999
RS = 4.20	
RS81	2012	Statistical Steganalysis Attacks	DWT	Improve security and distortion resistant	Embedding capacity not discussed	PSNR = 81.33	
RS82	2012	Statistical Steganalysis Attacks	DCT and AES	Increased security level for the steganography system	Embedding capacity not discussed	PSNR = 36.68
NC = 0.3906
SSIM = 0.5502	
RS83	2013	Statistical Steganalysis Attacks	DCT and LSB	Robust against low-pass filtering attacks	Embedding capacity not discussed	Uses Bit Error Rate (BER)	
RS84	2014	Stego image distortion	DCT	Robustness against histogram analysis attack	Very low embedding rate	BPP = 0.100
PSNR = 43.97
RS = 0.143	
RS90	2014	Statistical Steganalysis Attacks	IWT	Robustness against attacks and high imperceptibility	Embedding duration is comparatively higher	Capacity = 95%
PSNR = 35.06
SSIM = 0.8723	
RS68	2016	Statistical Steganalysis Attacks	RDH	Improved security when compared to other methods	Time execution rate is low and embedding capacity is limited	BPP = 0.700
NC = 0.6239
PSNR = 47.64	
RS85	2018	Trade-off between Security and Capacity	DCT	Maintains minimum detectability against blind steganalysis attacks	Embedding capacity increased by 16.7%	PSNR = 53.38
MSE = 2.927	
RS86	2018	Statistical Steganalysis Attacks	DCT	Better robustness against common image processing attacks	The embedding rate is low	BPP = 0.7000	
RS91	2018	Trade-off between Security and Capacity	IWT and LSB	Better imperceptibility and higher embedding capacity	High computational complexity	BPP = 3.3438
PSNR = 32.4385
RS = 0.3600	
RS76	2019	Stego Image distortion	RNS	High visual quality for stego image	Image distortion with higher payload	BPP = 0.500	
RS92	2019	Trade-off between Security and Capacity	IWT	Secure and robust against attacks	Time complexity for the proposed algorithm is high	BPP = 1.000
PSNR = 43.67
SSIM = 0.9546
	
RS87	2021	Statistical Steganalysis Attacks	DCT	Robustness against statistical analysis attacks	Low relative embedding rate	BPP = 0.6000
SSIM = 0.9878
NC = 0.0987	
RS88	2021	Stego Image distortion	DCT	Robustness against RS attacks	Relatively low embedding capacity	BPP = 0.1000
PSNR = 43.45	
RS93	2021	Trade-off between Security and Capacity	IWT	Robust against universal steganalysis attacks with higher embedding capacity	High computational complexity	BPP = 5.25
PSNR = 44.58
SSIM = 0.9426	
RS94	2021	Low embedding capacity	IWT, CVD and LSB	Withstand steganalysis attacks and high embedding rate	Image distortion detected	BPP = 2.63
PSNR = 38.85	
RS96	2021	Trade-off between Security and Capacity	IWT	Achieves higher level of security	Time complexity is relatively higher	BPP = 1.000
PSNR = 51.83
SSIM = 0.9964	
RS70	2022	Trade-off between Security and Capacity	RDH, PVO and Prediction Error Histogram Shifting (PEHS)	Resist RS steganalysis and provide secure data transmission	Computational complexity is high for the implementation	BPP = 1.677
PSNR = 46.61
RS = 74%	
RS73	2022	Low embedding rate and stego image distortion	RDHEI	Ensures losses data extraction	Distortion of image with higher embedding capacity	BPP = 0.4994
PSNR = 26.56
MSE = 0.3445	
RS74	2022	Statistical Steganalysis Attacks	RDH, Arnold Transform (AT), and DCT	High degree of robustness, imperceptibility, and visual quality of stego image	Low embedding rate	BPP = 1.000
RS = 0.0055
PSNR = 46.71
NC = 0.9944
SSIM = 0.9849	
RS78	2022	Stego image distortion	RNS	Boosts the anti-steganalysis capability	Low embedding rate	BPP = 0.4000	
RS89	2022	Statistical Steganalysis Attacks	DWT and Alpha Blending	High visual image quality and imperceptibility to withstand attacks	Low embedding capacity	BPP = 1.000
PSNR = 66.50
MSE = 0.1206	
RS95	2022	Statistical Steganalysis Attacks	IWT	Robustness against attacks with high imperceptibility	Embedding capacity not discussed	PSNR = 46.08
MSE = 0.5632
SSIM = 0.9900	

10.1371/journal.pone.0308807.t009 Table 9 Adaptive domain-based image steganography techniques.

Reviewed Study (RP)	Year	Problem/Issue	Technique/ Method	Strength	Limitation	Evaluation Metric Results	
RS67	2013	Stego Image Distortion	RDH	Higher visual image quality	Payload capacity is relatively low	BPP = 1.000
PSNR = 60.65
SSIM = 0.9813	
RS117	2013	Low embedding capacity	Field Programmable Gate Array (FPGA)	High payload capacity and image quality	Time complexity of the application is high	BPP = 4.000
PSNR = 45.65
MSE = 0.4564	
RS8	2014	Trade-off between Security and Capacity	GA	High Visual Image quality and high embedding capacity	Steganalysis attacks not simulated	BPP = 1.96
PSNR = 45.39	
RS9	2015	Stego Image Distortion	GA, Logistics Maps and LSB	Attains high level of security with less computational time	Low embedding capacity	PSNR = 51.33
MSE = 0.0032
SSIM = 0.9997	
RS1	2016	Statistical Steganalysis Attacks	GA	Increased payload capacity	Not robust against steganalysis attacks	PSNR mentioned but values not stated	
RS3	2016	Statistical Steganalysis Attacks	GA, LSB and AES	High image visual quality	Embedding capacity not discussed	PSNR mentioned but values not stated	
RS7	2016	Stego Image Distortion	GA and DCT	Less visual stego distortion	Robustness decreases with slight variation in pixel discontinuities	Capacity = 68.75%
PSNR = 52.78
MSE = 0.3428
NC = 0.9999	
RS27	2016	Trade-off between Security and Capacity	CNN, AES and LSB	Stego image quality and High imperceptibility	Training model time is high	BPP = 3.00
PSNR = 40.41
SSIM = 0.7200	
RS28	2016	Statistical Steganalysis Attacks	CNN, AES, LSB, and IWT	Improved image visual quality	Low embedding rate capacity	Capacity = 19%
PSNR = 59.51
MSE = 0.0728	
RS120	2016	Stego Image Distortion	Content Adaptive, MiPOD and LSB-M	High un-detectability against universal statistical analysis	Image distortion noticed and low embedding capacity	BPP = 0.5000
RS = 1.234%	
RS6	2018	Statistical Steganalysis Attacks	GA and LSB	Increased imperceptibility and high capacity	Not Robust against certain attacks	PSNR = 63
RS = 6.25%	
RS11	2018	Statistical Steganalysis Attacks	GAN and CNN	High imperceptibility and security against attacks	Low embedding capacity	BPP = 0.5123	
RS29	2018	Statistical Steganalysis Attacks	CNN	Possibility to detect corrupted cover image	Low embedding capacity and high training model time	Capacity = 19%
PSNR = 51
MSE = 0.4898
SSIM = 0.9998	
RS13	2019	Stego Image Distortion	GAN	High robustness against statistical attacks	Low embedding rate	BPP = 0.4000
	
RS30	2019	Statistical Steganalysis Attacks	CNN	Better security performance against steganalyzer	Embedding payload capacity is relatively low	BPP = 0.5000	
RS31	2019	Statistical Steganalysis Attacks	CNN and RDH	Robust against some statistical analysis	Low embedding payload capacity	BPP = 0.8
PSNR = 53.87
	
RS38	2019	Statistical Steganalysis Attacks	DL	High rate of invisibility	High model training and low embedding capacity	BPP = 0.500
PSNR = 32.17
MSE = 0.9832
SSIM = 0.9845	
RS2	2020	Low visual quality	GA and RNS	Robust against steganalysis and cryptanalysis	Embedding capacity not discussed	PSNR = 13.0036
MSE = 0.3683	
RS14	2020	Statistical Steganalysis Attacks	GAN	Improved security of adversarial images	Embedding rate and capacity not discussed	RS = 0.523
PSNR = 44.6	
RS32	2020	Trade-off between Security and Capacity	CNN, LSB and Fuzzy Logic	Provided high embedding capacity	Distortion noticed with increased capacity	Capacity = 47.86%
PSNR = 45.87
MSE = 0.4536
SSIM = 0.8451	
RS35	2020	Stego Image Distortion	CNN and LSB	Provide comprehensive resistance to steganalysis attacks	Embedding capacity was not discussed	PSNR = 50.73
MSE = 0.5494	
RS79	2020	Statistical Steganalysis Attacks	RNS, Mobile edge computing and IoT	Maintains high visual image quality and resist steganalysis	Relatively low payload capacity	BPP = 0.05
PSNR = 82.75
MSE = 0.0003
SSIM = 1.000	
RS5	2021	Stego Image Distortion	GA	Robust against steganalysis attacks	Low embedding capacity	BPP = 1
PSNR = 80.42
SSIM = 0.9988	
RS15	2021	Statistical Steganalysis Attacks	GAN	High security level against single image steganalysis	Image distortion with appreciable level of capacity increase	BPP = 0.4000
RS = 1.200	
RS16	2021	Statistical Steganalysis Attacks	GAN and Sparse Cover	High security improvement	Payload capacity limited	BPP = 0.5000
RS = 0.600	
RS17	2021	Statistical Steganalysis Attacks	GAN	High visual image quality and improved security	Payload capacity not discussed	PSNR = 44.47
MSE = 2.550
SSIM = 0.9900	
RS19	2021	Statistical Steganalysis Attacks	GAN	Improved security against CNN based steganalysis	Low embedding rate	BPP = 0.4000
	
RS23	2021	Statistical Steganalysis Attacks	GAN	High steganalysis security detection	High model training time	BPP = 0.400
PSNR = 35.67	
RS33	2021	Stego Image Distortion	CNN and Vernam Algorithm	High image visual quality	Noticeable distortions with increased bit length	BPP = 2.923
PSNR = 55.07
MSE = 0.2023
SSIM = 0.9531	
RS39	2021	Statistical Steganalysis Attacks	DL	High robustness against image modification	Run time efficiency of the algorithm is low	BPP = 0.800
	
RS77	2021	Statistical Steganalysis Attacks	RNS and CNN	High imperceptibility and improved security	Low embedding rate	BPP = 0.400
	
RS4	2022	Low visual quality	GA and IWT	High image visual quality and imperceptibility achieved	Payload capacity not good	BPP = 0.75
PSNR = 51.77
MSE = 0.4319
SSIM = 0.9968	
RS20	2022	Statistical Steganalysis Attacks	GAN and CNN	High improvement in imperceptibility and detection rate	Embedding capacity is low	BPP = 0.4000
	
RS21	2022	Stego Image Distortion	GAN	High improvement in security and resistance against statistical attacks	Robustness decreases with increasing bit length	BPP = 0.5
PSNR = 27.60
MSE = 0.0023
SSIM = 0.9853	
RS22	2022	Low visual quality	GAN	Robust against steganalysis attacks	The embedding capacity payload is low	BPP = 0.400
PSNR = 42.64
SSIM = 0.4984	
RS24	2022	Statistical Steganalysis Attacks	GAN and Neural Style Transfer	Robust against stegoexpose than existing methods	High model training time	BPP = 1.000
PSNR = 43.95
SSIM = 0.9950	
RS25	2022	Trade-off between Security and Capacity	GAN	Robustness and better security performance	The embedding capacity is very low	BPP = 0.400
	
RS26	2022	Statistical Steganalysis Attacks	GAN	Improves overall image system security and reduces loss of secret information	Distortion observed in stego image as payload increases further	BPP = 5.61
PSNR = 38.96
SSIM = 0.9800	
RS34	2022	Low visual quality	CNN and Slice Encryption	More payload capacity and ability to withstand various attacks	Message length could easily be estimated	Capacity = 30225 bits
PSNR = 55.48
MSE = 0.4322
SSIM = 0.9940	
RS36	2022	Trade-off between Security and Capacity	CNN and RDH	High embedding capacity with strong security features	High model training time	PSNR = 40.65
MSE = 0.0456
SSIM = 0.9800	
RS37	2022	Statistical Steganalysis Attacks	CNN and hash generation model	Better robustness and security	Inefficiency of searching the index database	BPP = 0.800	
RS40	2022	Statistical Steganalysis Attacks	DL	High security performance against modern steganalyzer	Learning stability is a bit lower comparatively	BPP = 0.500
	
RS10	2023	Statistical Steganalysis Attacks	LSB, ECC and GA	Robust against RS statistical steganalysis attacks	High Embedding payload capacity	BPP = 3.39
MSE = 0.0999
PSNR = 50.53
SSIM = 0.9983
RS = 0.2450	
RS12	2023	Low embedding capacity	Hamilton Path, GA	High robustness against attacks	High embedding capacity	BPP = 3
PSNR = 41.80	
RS18	2023	Statistical Steganalysis Attacks	GA, LSB	Robustness against attacks	High-Capacity payload	BPP = 3.5
PSNR = 46.07
SSIM = 0.9979	

In order to compare the superiority of each of the methods mentioned in Tables 7–9 over other methods listed and to demonstrate the efficiency of each method through the approved standards (ie Payload Capacity, measured in Bit Per Pixel (BPP) and Imperceptibility using Peak Signal to Noise Ratio (PSNR) and measured in decibel (dB)), a graphical representation is provided. See Fig 7.

10.1371/journal.pone.0308807.g007 Fig 7 Comparison of embedding capacity and security of image steganography techniques.

4.5 Security analysis of image steganography techniques

The security impact analysis examines the various identified techniques using some key parameters. Section 4.4 has already provided a detailed review of all the 125 publications retained for this study, which are presented along with their strengths and limitations. However, some other key indicators are relevant to determine how the various existing techniques can provide robustness and resistance against attacks and their overall security. This will also enable comparison among the reviewed papers using common standard metrics and parameters. The indicators assessed in this section include the image dataset employed for the experiment, type of data embedding process, data embedding style, secret image type, real-time implementation of a proposed algorithm or technique, application of cryptography protocol (encryption), data compression, values obtained for the PSNR, robustness against steganalysis attacks and the overall security of each technique.

Table 10 provides a detailed comparison of the various existing techniques reviewed which used grayscale images for the experiment whereas Table 11 provides a detailed comparison of the various existing techniques reviewed which used color images. The reviewed articles show that four benchmark datasets consisting of BOSS base, USC-SIPI, Seam Carving Original Q75, and 24 KODAK image Databases have widely been used. These databases contained specific images. The specific image dataset used by each reviewed article is reported. The data-hiding process is divided into spatial, transform, and adaptive domains. The data embedding style is divided into random and sequential. For secret image type, the categorizations are color or grayscale. Yes or no is used to represent whether the respective technique implemented the algorithm in real-time, whether encryption was applied to the secret data, and whether the secret data was compressed. Robustness against steganalysis attacks is divided into high, medium, and low. The specific parameters considered for the robustness are embedding process and style, secret image type, and encryption. Techniques that fully satisfy the evaluation criteria of the researchers considering the key parameters are rated high, those that partially satisfy are rated medium and those that least satisfy are rated low. Security of the reviewed articles is divided into good, average, and low. The overall security is evaluated by taking into consideration all the parameters previously discussed, most importantly PSNR values, Encryption, Real-time implementation, Compression, and embedding process. Other parameters discussed in section 4 (4.4) were also taken into consideration. The techniques that satisfy the maximum parameters as determined by the researchers are rated good. Those that satisfy the parameters partially are rated average, whereas those that least satisfy the key parameters are rated low. To avoid bias, the Delphi Expert Method [110] was adopted to evaluate the studies culminating in the rating provided for the robustness against attacks and overall security. All five researchers acted as experts and evaluated each study against the set of key parameters separately. Thereafter, a meeting was called to consolidate each rating. Where individual opinions differ, the cycle of Delphi was reinitiated until a consensus was reached. The method was designed in such a way that the researchers provided reasoning for individual responses. This was to help confirm the plausibility and strength of the individual researchers’ evaluation.

10.1371/journal.pone.0308807.t010 Table 10 Comparison of various existing image steganography techniques and methods for grayscale images.

Reviewed Paper (RS)	Dataset Used	Embed-
ding Process	Data Embed-
ding	Secret Image Type	Real-time	Encry-
ption?	Comp-
ression?	PSNR (dB)	Robustness Against Attacks	Security	
RS3	Lena	Adaptive	Random	Gray	No	Yes	Yes	N/A	Medium	Average	
RS5	Lena, Baboon, Peper, Lake	Adaptive	Random	Gray	Yes	No	No	80.42
	Medium	Good	
RS9	Lena, Lion	Adaptive	Random	Gray	Yes	No	No	51.33
	Medium	Average	
RS12	Lena	Adaptive	Random	Gray	Yes	Yes	No	41.8	Medium	Average	
RS13	Humanface	Adaptive	Random	Gray	Yes	No	Yes	N/A	Low	Average	
RS14	Building	Adaptive	Random	Gray	Yes	No	No	44.6	Medium	Low	
RS15	Building	Adaptive	Random	Gray	Yes	No	No	N/A	Low	Average	
RS16	Road, Building	Adaptive	Random	Gray	Yes	No	No	N/A	Low	Average	
RS18	Lena, Pepper, Baboon, Cameraman	Adaptive	Random	Gray	Yes	No	No	46.07	Medium	Average	
RS19	Building	Adaptive	Random	Gray	Yes	No	No	N/A
	Low	Low	
RS20	Building	Adaptive	Random	Gray	Yes	No	No	N/A	Medium	Low	
RS23	Building	Adaptive	Random	Gray	Yes	No	No	35.67	Medium	Low	
RS26	Building	Adaptive	Random	Gray	No	No	No	38.96
	Medium	Average	
RS30	Building	Adaptive	Random	Gray	Yes	No	No	N/A	Medium	Low	
RS31	Dog, Puppy, Laptop	Adaptive	Random	Gray	Yes	No	No	53.87
	Medium	Average	
RS32	Lena, Lion
Snow, Aeroplane	Adaptive	Random	Gray	Yes	Yes	No	45.87
	High	Average	
RS33	Lion	Adaptive	Random	Gray	No	Yes	No	55.07
	High	Average	
RS34	Lena, Coins, Baboon, Cameraman	Adaptive	Random	Gray	Yes	Yes	No	55.48
	High	Average	
RS35	Lena, Lion, Cameraman	Adaptive	Random	Gray	Yes	No	No	50.73
	Medium	Average	
RS40	Building	Adaptive	Random	Gray	Yes	No	No	N/A
	Medium	Low	
RS41	Lena, Cameraman, Pirates	Spatial	Sequential	Gray	No	No	No	39.39	Low	Average	
RS42	Imgaeset	Spatial	Sequential	Gray	Yes	No	No	52.52	Medium	Average	
RS43	Lena, Tiffany, Baboon, Jet, Bird, Castle, Pepper, Boat	Spatial	Random	Gray	Yes	Yes	No	37.66	High	Average	
RS44	Lena, Tiffany
House, Milk, Jet	Spatial	Sequential	Gray	No	No	Yes	41.58	Medium	Average	
RS45	Lena, House	Spatial	Sequential	Gray	No	No	No	N/A	Low	Low	
RS46	Lena, Pepper, Jet, Airplane
Truck, Tank, Baboon, Boat	Spatial	Sequential	Gray	Yes	Yes	Yes	36.38	High	Average	
RS49	Imageset	Spatial	Random	Gray	Yes	No	No	N/A	Low	Low	
RS50	Lena, Couple, Baboon, Boat
Pepper, Man, Tiffany,	Spatial	Sequential	Gray	Yes	No	No	33.83	Low	Low	
RS51	Lena, Couple, Baboon, Boat
Pepper, Man, Tiffany, baby	Spatial	Sequential	Gray	Yes	No	No	39.09	Low	Average	
RS52	Imgaeset	Spatial	Sequential	Gray	Yes	No	No	N/A	Low	Low	
RS53	Lena, Baboon	Spatial	Random	Gray	No	No	No	40.79	Low	Average	
RS54	Building	Spatial	Random	Gray	Yes	No	No	N/A	Low	Low	
RS55	Lena, Couple, Baboon, Boat
Pepper, Man Tiffany,	Spatial	Random	Gray	Yes	Yes	No	40.95	High	Average	
RS56	MRI Image	Spatial	Random	Gray	Yes	Yes	Yes	50.39	High	Good	
RS57	Airplane, Baboon	Spatial	Random	Gray	Yes	No	Yes	43.62	High	Average	
RS58	Building	Spatial	Random	Color	Yes	No	No	57.33	Medium	Average	
RS59	Lena, Couple, Baboon, Boat
Pepper, Man, Tiffany, baby	Spatial	Sequential	Gray	Yes	No	No	35.68	Low	Low	
RS60	Buildings	Spatial	Random	Gray	Yes	No	No	48.59	Low	Average	
RS61	Baboon Cameraman Airplane Goldhill Lena Peppers Tiffany Boat Aerial Clown Zelda	Spatial	Random	Gray	Yes	Yes	No	51.68	Medium	Average	
RS63	Baboon, Pepper, Airplane	Spatial	Random	Gray	Yes	No	No	48.61	Medium	Average	
RS64	Bossbase	Spatial	Random	Gray	Yes	No	No	57	Medium	Average	
RS66	Lena	Spatial	Sequential	Gray	Yes	No	No	50.51	Medium	Average	
RS67	Lena, Couple, Baboon, Boat
Pepper, Man, Tiffany, baby	Adaptive	Sequential	Gray	No	No	No	60.65
	Medium	Medium	
RS68	Imageset	Transform	Random	Gray	Yes	No	No	47.64	Low	Average	
RS69	Lena, Couple, Baboon, Boat
Pepper, Cameraman, Tiffany	Spatial	Random	Gray	Yes	Yes	No	31.99	High	Average	
RS70	Lena, Boat,
Pepper, Barbara, Goldhill	Transform	Sequential	Gray	No	No	No	46.61	Low	Average	
RS71	Lena, Couple, Baboon, Boat
Pepper, Cameraman, Tiffany	Spatial	Sequential	Gray	No	No	No	43.13	Low	Average	
RS72	Lena, F16, Boat, Zelda
Pepper, Lake, Barbara, Baboon	Spatial	Random	Gray	Yes	Yes	Yes	N/A	High	Average	
RS73	Lena, Zelda Couple, Boat, Pepper, Elaine, Lake Baboon	Transform	Random	Gray	Yes	Yes	No	25.56	High	Average	
RS76	Glasscup, Statue	Transform	Sequential	Gray	Yes	No	No	N/A	Low	Low	
RS77	Building	Adaptive	Random	Gray	Yes	No	No	N/A
	Low	Low	
RS78	Building	Transform	Random	Gray	Yes	No	No	N/A	Low	Low	
RS79	Deer, Boat, Cameraman	Adaptive	Random	Gray	Yes	No	No	82.75
	Medium	Good	
RS82	Building	Transform	Random	Gray	Yes	Yes	No	36.68	High	Average	
RS83	Lena, House, Baboon, Lily
Flowers	Transform	Sequential	Gray	No	Yes	No	N/A	Low	Low	
RS84	Lena, Boat, Baboon, House, Woman, Pepper	Transform	Sequential	Gray	Yes	No	No	43.97	Low	Average	
RS88	Lena	Transform	Random	Gray	Yes	No	Yes	43.45	High	Average	
RS90	Imageste	Transform	Sequential	Gray	Yes	Yes	Yes	35.06	High	Average	
RS91	Lena, Pepper, Baboon, Boat	Transform	Sequential	Gray	Yes	No	Yes	32.44	Medium	Average	
RS92	Lena, Tank, Elaine, Boat
Baboon, Couple, Airplane	Transform	Sequential	Gray	Yes	No	No	43.67	Low	Average	
RS93	Baboon	Transform	Sequential	Gray	No	No	No	44.58	Low	Average	
RS94	Lena, Woman	Transform	Random	Gray	Yes	No	No	38.85	Low	Average	
RS95	Lena	Transform	Random	Gray	Yes	No	No	46.08	Low	Average	
RS96	Lena, Woman, Baboon, Gun
Aeroplane, Man, Portrait	Transform	Random	Gray	Yes	No	No	51.83	medium	Average	
RS97	Chinese, Lena, English
Baboon	Spatial	Random	Gray	Yes	No	No	11.96	Low	Low	
RS98	Lena, Pepper, Cameraman
Baboon	Spatial	Sequential	Gray	No	No	No	49.40	Low	Average	
RS99	Lena, Baboon, Mandrill, Boat Barbara, Zelda	Spatial	Sequential	Gray	No	No	No	51.14	Medium	Average	
RS100	Lena, Baboon
Boat, Clown
Zelda	Spatial	Sequential	Gray	Yes	No	No	51.16	Medium	Average	
RS101	Lena, Pepper, Boat, Goldhill
F16, Baboon	Spatial	Sequential	Gray	Yes	No	No	31.69	Low	Low	
RS105	Baboon, Aeroplane	Spatial	Random	Gray	Yes	Yes	No	44.09	High	Average	
RS109	Imageset	Spatial	Sequential	Gray	No	No	No	N/A	Low	Low	
RS110	Baseboss	Spatial	Sequential	Gray	Yes	No	No	N/A	Medium	Low	
RS112	Flower, Lena, Rabbit, Garden	Spatial	Sequential	Gray	Yes	Yes	No	64.15	High	Good	
RS113	Building	Spatial	Random	Gray	Yes	Yes	No	N/A	Low	Low	
RS116	Baboon, Barbara, House,	Spatial	Sequential	Gray	Yes	Yes	Yes	40.09	High	Average	
RS117	Boat	Adaptive	Random	Gray	Yes	No	No	45.65
	Low	Average	
RS118	Lena, House Couple, Boat, Truck, Pepper, Female, Lake, Male Baboon, Splash, Cameraman	Spatial	Random	Gray	Yes	No	No	36.45	Medium	Low	
RS120	Hill, Chapel	Adaptive	Sequential	Gray	Yes	Yes	No	N/A	High	Average	
RS121	Lena	Spatial	Random	Gray	Yes	No	No	45.05	Low	Average	
RS123	Church, man	Spatial	Random	Gray	Yes	No	No	N/A	Low	Low	
RS125	Imageset	Spatial	Random	Gray	Yes	No	No	40.12	Low	Average	

10.1371/journal.pone.0308807.t011 Table 11 Comparison of various existing image steganography techniques and methods for color images.

Reviewed Paper (RS)	Dataset Used	Embed-
ding Process	Data Embed-
ding	Secret Image Type	Real-time	Encry-
ption?	Comp-
ression?	PSNR (dB)	Robustness Against Attacks	Security	
RS1	Baby, Pigeon, Flower	Adaptive	Random	Color	No	No	No	N/A	Low	Low	
RS2	Lena, pepper	Adaptive	Random	Color	Yes	Yes	No	13.0036	Medium	Low	
RS4	Lena, Pepper, Baboon	Adaptive	Sequential	Color	No	No	No	51.77
	Medium	Average	
RS6	Paper	Adaptive	Random	Color	No	No	No	63
	Medium	Avera	
RS7	Monkey, Flower	Adaptive	Random	Color	No	No	No	52.78
	Low	Average	
RS8	Lena, Pepper, Aeroplane,
Baboon	Adaptive	Random	Color	No	No	No	45.39	Low	Average	
RS10	Lena	Adaptive	Random	Color	Yes	Yes	Yes	50.53	High	High	
RS11	Humanface	Adaptive	Random	Color	Yes	No	No	N/A	Medium	Average	
RS17	Bridge	Adaptive	Random	Color	No	No	No	44.47
	Low	Average	
RS21	Flowers, Frog	Adaptive	Random	Color	Yes	No	No	27.60
	Medium	Low	
RS22	Bird,
Humanface	Adaptive	Random	Color	Yes	No	No	42.64
	Medium	Average	
RS24	Imagenet	Adaptive	Random	Color	Yes	No	No	43.95
	Medium	Average	
RS25	Woman	Adaptive	Random	Color	Yes	Yes	No	N/A
	Low	Average	
RS27	Flower, baby	Adaptive	Radom	Color	Yes	Yes	No	40.41
	Medium	Average	
RS28	Woman	Adaptive	Random	Color	Yes	Yes	No	59.51
	High	Average	
RS29	Lena	Adaptive	Random	Color	Yes	No	No	51
	Medium	Average	
RS36	Imageset	Adaptive	Random	Color	No	No	No	40.65
	Low	Average	
RS37	Seabird	Adaptive	Random	Color	Yes	Yes	No	N/A	Medium	Low	
RS38	Wordnet	Adaptive	Random	Color	Yes	Yes	No	32.17
	Medium	Low	
RS39	Baby with Piano	Adaptive	Sequential	Color	No	No	No	N/A
	Low	Low	
RS47	Lena, Strawberry	Spatial	Sequential	Color	Yes	No	No	47.99	Low	Average	
RS48	Lena	Spatial	Sequential	Color	Yes	No	No	25	Low	Low	
RS58	Building	Spatial	Random	Color	Yes	No	No	57.33	Medium	Average	
RS62	Imageset	Spatial	Random	Color	Yes	No	No	N/A	Low	Low	
RS65	Imageset	Spatial	Random	Color	No	No	No	46.89	Low	Average	
RS74	Pepper, Boat
Baboon, Aeroplane	Transform	Random	Color	Yes	No	No	46.71	Low	Average	
RS75	Lena, Pepper	Spatial	Sequential	Color	Yes	No	No	51.93	Medium	Average	
RS80	Lena	Transform	Random	Color	Yes	No	No	58.95	Medium	Average	
RS81	Sea, Grass	Transform	Sequential	Color	Yes	No	No	81.33	Medium	Good	
RS85	Lena, Baboon
Pepper	Transform	Random	Color	Yes	No	Yes	53.38	High	Average	
RS86	House, Toy
Man	Transform	Sequential	Color	Yes	No	No	N/A	Low	Low	
RS87	Bird	Transform	Random	Color	Yes	No	No	N/A	Low	Low	
RS89	Lena, Pepper
Cameraman, Baboon	Transform	Random	Color	Yes	Yes	Yes	66.50	High	Good	
RS102	Lena, Apple Airplane, Baboon,	Spatial	Sequential	Color	Yes	No	No	56.44	Medium	Average	
RS103	Imageset	Spatial	Random	Color	No	Yes	No	74.02	High	Good	
RS104	Butterfly	Spatial	Sequential	Color	No	No	Yes	54.08	Medium	Average	
RS105	Baboon, Aeroplane	Spatial	Random	Gray	Yes	Yes	No	44.09	High	Average	
RS106	Sea, Cow, Tree, House
Church,	Spatial	Sequential	Color	Yes	No	Yes	48.24	Medium	Average	
RS107	Lena, Pepper, Baboon	Spatial	Random	Color	Yes	Yes	Yes	89.03	High	Good	
RS108	Lena, Baboon
Aeroplane, Girl	Spatial	Random	Color	No	No	No	83.27	Medium	Good	
RS111	Baboon, building, Woman	Spatial	Random	Color	Yes	Yes	No	85.664	Medium	Average	
RS114	Lena, Apple, Butterfly
Church, Orange	Spatial	Random	Color	No	Yes	No	48.35	High	Average	
RS115	Deer	Spatial	Random	Color	No	Yes	No	49.56	High	Average	
RS119	House, Lake Pepper, Baby, Baboon, Image1,	Spatial	Random	Color	Yes	Yes	Yes	83.99	Medium	Good	
RS122	Lena, Baboon, Pepper, man	Spatial	Random	Color	Yes	No	No	69.45	Medium	Average	
RS124	Man	Spatial	Random	Color	Yes	Yes	No	30.30	High	Average	

4.6 Future scope and research directions for image steganography

The challenge of image steganography remains to achieve high embedding payload capacity while maintaining robustness, distortion resistance, imperceptibility, and overall security (un-detectability). This challenge still exists in many of the reviewed works. The existing systems suffer from low embedding rate, low visual quality of stego image, image distortion, high computational complexity, performance accuracy, low throughput efficiency, as well as detection and modification of secret data. These gaps are largely due to the techniques employed by the existing works. Other identified gaps in most of the existing works are vulnerabilities such as double-frequencies, zero points, and non-accurate detection of statistical steganalysis results. These vulnerabilities have been extensively exploited by steganalysers.

Several of the reviewed works have no layer of protection against unauthorized access to secret data. This is because many of the existing works did not apply cryptographic protocols. Those that implemented cryptography for encryption and decryption are also based on the raster order LSB substitution method which is prone to RS statistical steganalysis attacks [111]. From Tables 10 and 11, only 34 out of the 125 reviewed papers employed encryption (cryptography). This represents 27% of all reviewed papers. The key aim of image steganography technique is to hide the existence of secret data using cover objects (audio, video, image, text, network) [112,113]. Also, for steganography to achieve its aim, the transferred message on the recipient side should be the same as the original message without noticeable suspicion by a third party [114,115]. Embedding secret data into the cover object does not provide the security needed [116–118]. This is because, an unauthorized person can read the message when the cover image is attacked, breaking the requirement for confidentiality of the message.

The analysis of the previous works has shown that there is a need to put in place appropriate corrective measures to strike an adequate balance between high payload and security against statistical steganalysis including RS attacks. Thus, techniques that achieve higher payload capacity and better-corrected pixels in ensuring enhanced security protection of secret data in storage and transmission are required. One key challenge of the image steganography embedding process is the secret message size [119]. This challenge could be overcome by employing lossless compression algorithm techniques to achieve higher payload capacity and high embedding rate [120]. From Table 10, only 13% of all the reviewed articles in this study implemented data compression. Compression reduces the secret data size before embedding process begins [121,122].

Clearly, this systematic literature review has shown that the research direction in image steganography has been broad and diverse since 2012. As challenges in image steganography continue, the research domain also continues to evolve. Aside from the traditional methods, researchers have begun experimenting other areas of application for image steganography. For example, Table 4 shows that 9 of the papers adopted other different techniques than the known traditional methods for steganography. This can be inferred that scholars within the image steganography domain are exploring newer and more innovative approaches.

Future research directions could enhance the security and robustness of image steganography applications by:

Cryptographic protocols as a layer of security protection. Higher security and robustness in image steganography can be achieved using multiple encryptions to mask and scramble the content of the secret message before embedding. Encrypted embedded secret data have more ability to resist steganalysis.

Future research could explore compression and image enhancement techniques to achieve a high payload while maintaining image visual quality. This could help solve the problem of balancing the tradeoff between security and embedding capacity

Future research could utilize other novel techniques from domains that have the propensity to achieve computationally efficient, reduced computational complexity, improved performance, and undetectability which are the major issues advocated for by researchers within the image steganography domain. For instance, imperceptibility and security could be improved by employing emerging technologies such as Blockchain Technology [123]. Stego-images containing secret data are often transmitted over unsecured public networks, thereby making the secret data susceptible to many attacks including man-in-the-middle attacks, tampering, and eavesdropping [124,125].

Blockchain technology could be employed in image steganography to ensure stego-images are more secure and authenticated [114]. This is because, blockchain has immutable properties, easy traceability, tracking capabilities, and transparency [50]. In addition, future research could rely on emerging artificial intelligence and machine learning power technologies such as ChatGPT to provide robust techniques against steganographic attacks.

5. SLR results discussion and implication

The review focused on providing evidence on image steganography techniques that have been designed to resist statistical steganalysis attacks. The review has shown that several such techniques and methods, with the capability to withstand complex attacks, exist. This systematic literature review was based on key questions that provided a foundation for the review. The SLR results are provided as summarized answers to the study’s research questions. Table 12 provides the questions and a summary of the systematic literature review results.

10.1371/journal.pone.0308807.t012 Table 12 Answers to SLR questions and summary of review results.

Item	Research Questions (RQ)	Answers to Research Questions	
RQ1	Q1. What have been the Trends in Publication of Image Steganography Applications?	The review of all the articles revealed an interesting result for image steganography research. Intriguingly, research on image steganography is skewed in terms of publication trend. The skewness in the publication trend for image steganography can be seen in analysis concerning the year of publication, publication outlets, country of origin of corresponding author, and application domains for image steganography. More than 50% of articles were published after 2020. IEEE Explore is the most preferred destination for scholars researching image steganography, while majority of the articles emanated from India and China with no single article from Sub-Saharan Africa (SSA), indicating that research in the field of steganography is low in Africa.	
RQ2	Q2. What Methods and Techniques are Used in Image Steganography for Resisting Statistical Attacks?	After reviewing the articles, Generative Adversarial Networks (GAN) was observed as the most preferred image steganography technique, and machine learning based algorithms such as DL, CNN, and GA have dominated image steganography research. The results revealed that adaptive methods are overtaking spatial and transform domain approaches. Previously preferred traditional techniques such as LSB, PVD, DCT and IWT algorithms are receiving less attention in image steganography research and applications.	
RQ3	Q3. What are the Standard Performance Evaluation Metrics for Image Steganography Techniques	The review of all the articles revealed several performance metrics that have been used to evaluate image steganography techniques. Most of the articles used more than one performance of evaluation metrics. Five performance evaluation metrics were observed to be commonly used by majority of the studies reviewed. These metrics are PSNR, MSE, SSIM, NC, and BPP. Few of the articles did not discuss performance evaluation metrics.	
RQ4	Q4 What Security Impact Has the Techniques have on Image steganography for Resisting Statistical Attacks?	The reviewed articles show that four benchmark datasets consisting of BOSS base datasets, USC-SIPI, Seam Carving Original Q75, and 24 KODAK image Databases have widely been used. Adaptive embedding techniques such as GAN, GA and CNN were resistant to geometrics attacks, and statistical detection analysis attacks such as RS and Histogram analysis attacks. The visual quality of adaptive based methods and undetectability of secret message were high and robust against noise cropping and less prone to image rotation. However adaptive methods have limited embedding capacity. However, even though spatial domain techniques such as LSB, LSB-M, PVD have high embedding capacity and visual quality, they are highly prone to noise cropping, rotation, non-structural detection analysis and statistical detection analysis attacks such as RS and Histogram analysis attacks. Spatial domain techniques are also vulnerable to geometric attacks. Transform domain techniques such as DCT and IWT offered high security consideration than spatial domain methods but less effective when compared to adaptive embedding methods. Only few of the techniques have also been implemented in a real-time application.	
RQ5	Q5. What are the Future Scope and Research Direction for Image Steganography?	The review has shown that research direction in image steganography have been broad and divergent since 2012. As challenges in image steganography continue, research domain also continues to evolve. Aside the traditional methods, researchers have begun experimenting other areas of application for image steganography. The challenge of image steganography remains achieving high embedding payload capacity while maintaining robustness, distortion resistance, imperceptibility, and overall security (un-detectability). It is therefore recommended that researcher may consider emerging technologies such as blockchain technology, artificial neural networks, encryption, and compression in future research works to improve security and embedding capacity.
	

5.1 Research trends in image steganography techniques

The review reveals an interesting result for image steganography research. Intriguingly, research on image steganography is skewed in terms of publication trends. The skewness in the publication trends for image steganography can be seen in analysis concerning the year of publication, publication outlets, country of origin of the corresponding author, and application domains for image steganography. The research shows that despite the growing interest in the research field in image steganography, research in the area took a sharp nosedive in 2020, but rather experienced astronomical expansion from 2021 to 2023. Approximately half of the papers studied in this research were published from 2021 to 2023. Indeed, cyber-attacks on organizations and individual data due to inherent vulnerabilities in network security protection were expanding [126], even before 2020. This might have contributed to the interest of researchers in this domain to find solutions to the ever-increasing threat. The volume of research conducted in this domain post-COVID-19 is not surprising, as the Coronavirus (COVID-19) pandemic resulted in an increased number and range of cyber-attacks resulting in personal and organizational data breaches and compromises [127]. The exponential increase in the research domain could be a direct response to the increasing trend of cyber-attacks during the COVID and the need for companies to work remotely as a means of cutting costs and making use of investments in technology during the pandemic. In terms of publication outlets, it is interesting to note that more than half of the articles reviewed were published in IEEE. The implication is that IEEE has become the destination of choice for researchers publishing studies on image steganography. This finding corroborates the study of Kaur et al., [50] where most of the reviewed papers were also published in IEEE. This brings to the fore the need to address the dominance in the publication of such crucial research areas by a particular publication house and expand the domain in other publication outlets. Although there are several publication outlets that publish research on image steganography, such outlets were dully not represented in this study. Given that image steganography techniques for resisting steganalysis have become a growing area of research interest, other publication outlets may put in place measures to attract researchers. This could include special issues concerning the domain and putting in place incentives to attract researchers. Surprisingly, despite the growing cases of cybercrimes in Sub-Sahara Africa [128], the interest of researchers in this geographic location is low. It must, however, be mentioned that researchers in Sub-Saharan Africa have begun showing interest in publishing in this area, as evidenced by a recent publication [70]. Developing research capabilities including collaboration with external scholars particularly those in India and China could ameliorate the low level of research by African scholars in this domain. The digital divide in Africa is growing. Internet penetration in Africa is also expanding, and as a result, digital crimes have increased. Developing research capabilities and acquiring the requisite technical knowledge to research image steganography techniques could prevent many of the data breaches and cyber-attacks as well as save African-based organizations from data breaches and compromises.

5.2 Image steganography techniques for resisting steganalysis

As observed in Fig 6 and Table 4, Generative Adversarial Neural Networks (GAN) is the most preferred image steganography technique for resisting steganalysis attacks. This finding supports arguments by Liu et al., [129] that GAN has seen increasing achievement in the field of image steganography, computer vision, and natural language processing. From the review, the application of GAN in image steganography witnessed exponential growth between 2018 and 2022. GAN was first proposed in 2014 [130] and has seen great application in many fields of Computer Science. In image steganography, it improves security by resisting cover modification, enhances the cover selection and synthesis processes, and achieves overall security protection against steganalysis attacks. The security capabilities of GAN are higher than other adaptive methods and traditional spatial and transform domain methods [131]. Quite interestingly, despite the complexity associated with GAN-based image steganography approaches, the technique has seen overwhelming applications. The increase in the use of GAN processes is attributed to recent developments in deep learning-based steganalysis [132–134]. GAN has the capability to resist state-of-the-art deep learning-based steganalysis [135]. GAN also can be used to improve the security performance of image steganography techniques in other domains including spatial domain applications. These capabilities make GAN a considerable option for image steganography regardless of the complexity associated with it.

The study further shows that machine learning-based algorithms are recently dominating image steganography research. This confirms the argument by Hussain et al., [82] on the growth of machine learning techniques including GAN, DL, CNN, and GA. These machine learning-based algorithms have emerged as powerful tools for image steganography capable of resisting steganalysis attacks. Subramanian et al, [131] argue that machine learning-based algorithms will continue to see greater applications in future image steganography works. DL, GA, and CNN like other machine learning algorithms including GAN are great techniques for fooling steganalysis and preventing them from detecting secret images hiding in cover images. In addition to machine learning-based algorithms, the study reveals that researchers are exploring many other areas of application for image steganography. At least 9 of the reviewed articles were based on other methods rather than known traditional steganography methods or machine learning methods.

The overall implication is that previously preferred image steganography techniques particularly the least significant bit (LSB) insertion algorithms are becoming unpopular among data protection and information security researchers. This finding supports the assertion by Subramanian et. al., [131] that traditional algorithms like LSB are now receiving less attention in image steganographic applications. Between the spatial domain and transform domain, algorithms based on the spatial domain were more. This finding supports arguments by Hussain et al., [82] that the spatial domain methods for secret data embedding are more popular than the transform domain due to the easiness of embedding and extraction of data in the spatial domain. The spatial domain however suffers from less robustness. The major spatial domain methods include LSB, LSB-M, AED, PVD, and PH. The major transform domain methods identified were DCT and IWT techniques such as RDH and RNS however saw application across the various embedding domain processes (ie spatial, transform, and adaptive domains). Indeed, LSB is considered the fundamental and conventional steganography method capable of hiding a larger secret message in a cover image without noticeable visual distortions. Over time, different variations of LSB have been developed. The disadvantage of LSB is that an increase in payload reduces the overall visual quality making it an easy target for attacks. Given the challenges of LSB, Wu and Tsai [136] proposed PVD using the difference between two neighboring pixels to determine the number of secret bits to be embedded. Since then, many steganographic methods have been proposed to improve the initial PVD method. From the study, it can further be observed that AED is one of the prominent embedding strategies in the spatial domain. AED schemes have the capability to maintain minimum visual quality and are noted to provide higher imperceptibility when compared to other spatial domains [137]. From Table 4, AED recorded the second highest techniques for image steganography. Different hybrid edge-based methods including combining canny edge and fuzzy edge adaptors [138,139] were observed in the articles reviewed for this study. The study has revealed varied techniques for protecting data against attacks. However, more research investigations are required to identify how emerging technologies including artificial neural networks (ANN) could be explored to provide harmonized security capabilities against statistical steganalysis attacks.

5.3 Security performance of image steganography against attacks

The systematic review results revealed that the most significant contribution of steganography techniques is resistance against statistical detection analysis attacks such as Regular-Singular (RS) and Histogram analysis attacks. Adaptive embedding techniques such as GAN, GA, and CNN and transform domain techniques including DCT and IWT methods were hard to expose to such statistical detection analysis attacks. However, spatial domain techniques including LSB and PVD were easy to expose. Most of the studies reviewed reported improvement against RS and histogram analysis attacks, indicating continued research improvement in overcoming these types of attacks. Another key significance of existing steganographic techniques is resistance against non-structural detection attacks. Machine learning-based algorithms proved difficult to detect by non-structural detection attacks, whereas spatial domain and transform domain methods were easily detectable. In terms of geometric attacks, it was observed that adaptive embedding techniques such as GAN and CNN and techniques-based transform domain methods were resistant and hard to geometric attacks while spatial domain methods were vulnerable to such attacks.

The visual quality of adaptive-based methods and the undetectability of secret messages were high and robust against noise cropping and less prone to image rotation. However adaptive methods have limited embedding capacity. Even though spatial domains such as LSB, LSB-M, and PVD have higher payload capacity and visual quality, they are highly prone to noise cropping, and rotation. Overall, most of the reviewed studies reported higher SI visual quality, an important measure in ensuring the transmission of secret data is not detectable by the HVS. Transform domains such as DCT and IWT offered higher security considerations than spatial domain methods but were less effective when compared to adaptive embedding methods. Only a few of the techniques have also been implemented in a real-time application. When evaluation of image steganography is done using capacity, traditional embedding algorithms including the various variations of LSB offer higher embedding capacity than machine learning-based techniques such as CNN, GAN, and DL.

Despite the notable progress achieved in image steganographic techniques, computational complexity and time complexity were observed to be a major challenge in all the reviewed papers. Even though computational complexity is a generic challenge as most studies indicated, adaptive embedding techniques such as CNN, DL, and GAN were reported to have higher computational complexity results than both spatial domain and transform domain methods. This finding is, however, not surprising given that most of the adaptive embedding approaches were based on machine learning techniques. This is because, one key challenge associated with machine learning algorithms has been identified to be computational complexity [140,141]. The challenge of computational complexity is noted to significantly have a direct impact on image steganography techniques with respect to computational speed thereby having a tremendous impact on the performance of emerging image steganography applications. This notwithstanding, recent studies have reported measures to improve the computational complexity and time accuracy of machine learning algorithms [142]. This has occasioned the growing use of genetic algorithms (GA) in image steganography applications [70], as GA has been noted as reducing the computational complexities of machine learning-based algorithms.

From Tables 10 and 11, the results from the systematic review analysis have shown the positive effects of combining steganography and cryptography. The analysis further shows that image steganography studies that had implemented cryptography were rated high for robustness and good for overall security. The combined effects of cryptography and steganography provide an additional layer of protection for the privacy system against many security attacks [143,144]. Although the combination is noted as an extra payload on the time and space complexities of the application, it offers comparative advantages in terms of robustness, confidentiality, and privacy [145]. However, several techniques have recently been introduced to reduce the computational cost performance associated with the art of combining steganography and cryptography.

From Fig 7, Modified Least Significant Bits (M-LSB) had the highest PSNR value indicating the highest imperceptibility. This was obtained for RS 108. This was followed by RS111 with a PSNR value of 85, which utilized the LSB technique. For embedding capacity, the highest capacity recorded among the reviewed articles was 8.88BPP for the PVD technique. This was obtained in RS48. This was followed by RS26, a generative adversarial network (GAN) which obtained 5.61BPP. A careful examination of Tables 7–9 shows that Spatial domain techniques recorded the highest imperceptibility outcome. However, spatial domains are susceptible to steganalysis attacks. The average highest embedding capacity was recorded in the spatial domain and transform domain techniques. Genetic Algorithm (GA) and GAN applications under the adaptive domains showed the best results for balancing embedding capacity and robustness. This explains the growing use of GAN and GA algorithms. Even though, the high-capacity trade-off to security and robustness improvement remains a challenge [146–148], GAN, GA, and other emerging technologies such as generative artificial intelligence (AI) have the potential to overcome the challenge.

6. Conclusion, research validity, and limitation

The paper provided a systematic literature review of image steganography techniques that can withstand statistical steganalysis attacks. To the best of the Authors’ knowledge and understanding of the existing literature, this systematic review is the first to have considered the entire spectrum of image steganography methods and techniques and their application in resisting steganalysis attacks. The study sampled 125 articles from four reputable electronic databases comprising ACM, IEEE, Science Direct, and Wiley. Using PRISMA for literature mapping, the articles were synthesized and analyzed using quantitative and qualitative methods. Trends in publication, techniques and methods, performance evaluation metrics, and the security impact of image steganography techniques against steganalysis were discussed. Extensive comparisons were drawn among existing techniques to evaluate their merits and limitations. Various future research directions in image steganography have been provided to help researchers who may want to consider emerging technologies to enhance data protection and security.

Research validity is an important component in all studies, as biases have the potential to negatively impact the study outcome. The possible biases and the threat to the validity of this research emanate from the potential omission of articles in the selection and data extraction processes. Various databases and journals publish research on cryptography and steganography, which may contain relevant articles that meet the inclusion criteria for the study. However, the article selection was limited to four databases only. It therefore becomes difficult to generalize the study findings. Nonetheless, the use of PRISMA guidelines for the article selection, coupled with the developed protocol by the authors which guided the various processes of data extraction significantly reduced the number of omitted articles and ultimately eliminated possible biases associated with the research validity. Also, a preliminary search conducted on Google Scholar, Citeseer, and SCOPUS identified, IEEE Explore, ACM Digital Library, ScienceDirect, and Wiley Online as the most appropriate databases containing many of the studies on image steganography techniques. The quality assessment metrics used for the data extraction further reduced biases. The keywords developed were also aimed at reducing biases. Ultimately, the objective was to ensure the articles selected were of good quality.

In conclusion, it was observed that GAN has become the most preferred image steganography technique, and machine learning-based algorithms such as DL, CNN, and GA are recently dominating image steganography research. The implication is that previously preferred traditional techniques such as LSB, DCT, and IWT algorithms are receiving less attention in image steganography. Future research could explore emerging technologies such as blockchain technology and artificial neural networks to strike an adequate balance between imperceptibility, robustness, and enhanced security for data protection on one hand, and high embedding payload capacity on the other hand.

Supporting information

S1 Appendix Detailed list of reviewed studies (RS).

(DOCX)

S2 Appendix PRISMA 2020 checklist for the study.

(DOCX)

S3 Appendix Data sources retrieved from electronic databases.

(XLSX)

10.1371/journal.pone.0308807.r001
Decision Letter 0
Solak Serdar Academic Editor
© 2024 Serdar Solak
2024
Serdar Solak
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
17 May 2024

PONE-D-24-14917Image Steganography Techniques for Resisting Statistical Steganalysis Attacks: A Systematic Literature ReviewPLOS ONE

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Add more recent reference to enhance introduction section. Discuss the state-of-art techniques with their merits and issues. The literature should be developed and, if possible, presented in more papers published in last years. Discuss the research gaps and relate how the proposed work has improved them. In the introduction section, include spatial domain methods in your article. Also mention the difference between frequency and spatial domain methods For examples, “High embedding capacity data hiding technique based on EMSD and LSB substitution algorithms”, “Image steganography based on LSB substitution and encryption method: adaptive LSB+ 3”, “A New Dual Image Based Reversible Data Hiding Method Using Most Significant Bits and Center Shifting Technique” and “Data Hiding Based on Frequency Domain Image Steganography”

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What solution you propose to make the system more robust. What is your difference from similar studies?

Prepare and explain your numerical examples more clearly for data hiding and data extraction processes.

In the study, it is necessary to present complexity analysis and security analysis. For security tests, histogram analysis and PDH can be given.

You must also provide numeric values. Rewrite the conclusion with following comments:(a) Highlight your analysis and reflect only the important points for the whole paper. (b) Mention the implication in the last of this section. Please, carefully review the manuscript to resolve these issues. (c) This section should be supported with numerical values.

There are grammatical errors in the article.

Reviewer #2: 1-The abstract contains more information than it should, for example, the sites that have been approved for papers and future work for the research presented. It is best to approach this matter in summary.

2- Please provide the researcher's contributions in point form

3- The equations listed are not referenced to the references on which they were based. In addition, it would have been better to include performance evaluation metrics in a table showing each measure and its equation

4- Is it possible to include a figure showing the superiority of the methods mentioned in Table 9 over other methods listed in the same table to demonstrate the efficiency of each method through the approved standards?

5- Is it possible to arrange the references in Table 9 according to the year of publication for ease of follow-up?

6- In Table 10, please add a field that shows the ratio of the number of images used to the maximum number of images used and discuss this matter due to its importance. I find it best to separate the table into two tables, the first for grayscale images and the other for color images for easy follow-up.

7-Rewriting in point form of Section 4.6 Future scope and research directions for image steganography

8- The research still needs to include other research that uses biometric techniques to hide images, such as voice or facial features. Please add such research to existing references

Reviewer #3: This paper presents an interesting image steganography overview. It covered systematic literature review of many stego techniques capable of resisting steganalysis attacks sampled from ACM Digital Library, IEEE Explore, Science Direct, and Wiley. The systematic review and Meta-Analyses have been synthesized and analysed using quantitative and qualitative methods. The works security and robustness are having significance, but the overall research presentation needs to be slightly improved covering some current aspects in order to be ready as publication, as noted within the following points that have to be fulfilled:

1- Give more elaboration on the real need for utilizing this Generative Adversarial Networks stego approaches. What is wrong in the normal other related stego methods requiring this kind of complex research. Try to support your explanation via real-life examples.

2- The study is recommended to consider the following image steganalysis researches within its coverage:

== "Integrating machine learning and features extraction for practical reliable color images steganalysis classification", Soft Computing 27(19):13877-13888 (2023)

== "Towards improving the performance of blind image steganalyzer using third-order SPAM features and ensemble classifier", Journal of Information Security and Applications 76:10354, Elsevier (2023)

== “Is blind image steganalysis practical using feature-based classification?”. Multimedia Tools and Applications (MTAP) 83(2): 4579–4612 (2024)

3- The image security review presented is very promising showing its high capacity trade-off to security and robustness improvement as challenging processes. Try to benefit from the image stego secrecy researches below connecting to the capacity vs secrecy of stego hiding attempts provided:

== "Efficient Reversible Data Hiding Multimedia Technique Based on Smart Image Interpolation", Multimedia Tools and Applications (MTAP) 79(39):30087-30109 (2020)

== "Novel Embedding Secrecy within Images Utilizing an Improved Interpolation-Based Reversible Data Hiding Scheme", Journal of King Saud University - Computer and Information Sciences, 34(5):2017-2030 (2022)

==  "Efficient Implementation of Multi-image Secret Hiding Based on LSB and DWT Steganography Comparisons", Arabian Journal for Science and Engineering (AJSE) 45:2631–2644 (2020)

== "High performance image steganography integrating IWT and Hamming code within secret sharing", IET Image Processing, 18(1): 129-139 (2024)

== “Dynamic smart random preference for higher medical image confidentiality”, Journal of Engineering Research (JER) 11(3A): 100-111 (2023)

== "Vibrant Color Image Steganography using Channel Differences and Secret Data Distribution", Kuwait Journal of Science and Engineering (KJSE) 38(1B):127-142 (2011)

==  "Pixel Indicator Technique for RGB Image Steganography", Journal of Emerging Technologies in Web Intelligence (JETWI) 2(1):56-64 (2010)

== “Improving grayscale steganography to protect personal information disclosure within hotel services”, Multimedia Tools and Applications (MTAP) 81(21): 30663–30683 (2022)

== "Improving data hiding within colour images using hue component of HSV colour space", CAAI Transactions on Intelligence Technology, IET (IEE) - Wiley, 7(1): 56–68 (2022)

==  "Efficient Image Reversible Data Hiding Technique Based on Interpolation Optimization", Arabian Journal for Science and Engineering (AJSE) 46(9):8441–8456 (2021)

== "Trustworthy image security via involving binary and chaotic gravitational searching within PRNG selections", International Journal of Computer Science and Network Security (IJCSNS) 20(12):167-176 (2020)

4- Combining cryptography and steganography is used a lot in literature as very promising philosophy to stand the privacy system against many security attacks. Although this combination is noted as extra payload in this direction, briefly link your review study work to the complexity combinations within the following studies:

== "Enhancing Medical Data Security via Combining Elliptic Curve Cryptography with 1-LSB and 2-LSB Image Steganography", International Journal of Computer Science and Network Security (IJCSNS) 20(12):232-241 (2020)

== "Protecting Medical Records against Cybercrimes within Hajj Period by 3-layer Security", Recent Trends in Information Technology and Its Application 2(3):1–21 (2019)

== "Enhancing Medical Data Security via Combining Elliptic Curve Cryptography and Image Steganography", International Journal of Computer Science and Network Security (IJCSNS) 20(8):1-8 (2020)

== "Securing Matrix Counting-Based Secret-Sharing Involving Crypto Steganography", Journal of King Saud University - Computer and Information Sciences, ISSN:1319-1578, 34(9): 6909–6924 (2022)

== "Watermarking Images via Counting-Based Secret Sharing for Lightweight Semi-Complete Authentication",  International Journal of Information Security and Privacy (IJISP) 16(1): 1-18 (2022)

== "Increasing Participants Using Counting-Based Secret Sharing via Involving Matrices and Practical Steganography", Arabian Journal for Science and Engineering (AJSE), 47(2): 2455–2477 (2022)

== "Refining image steganography distribution for higher security multimedia counting-based secret-sharing", Multimedia Tools and Applications (MTAP) 80:1143–1173 (2021)

== "Enhancing PC Data Security via Combining RSA Cryptography and Video Based Steganography", Journal of Information Security and Cybercrimes Research (JISCR) 1(1):5-13 (2018)

== "Compression Multi-Level Crypto Stego Security of Texts Utilizing Colored Email Forwarding", Journal of Computer Science & Computational Mathematics (JCSCM) 8(3):33-42 (2018)

== “3-Layer PC Text Security via Combining Compression, AES Cryptography 2LSB Image Steganography”, Journal of Research in Engineering and Applied Sciences (JREAS) 3(4):118-124 (2018)

== "Enhancing Speed of SIMON: A Light-Weight-Cryptographic Algorithm for IoT Applications", Multimedia Tools and Applications (MTAP) 78:32633–32657 (2019)

Reviewer #4: Title:

Image Steganography Techniques for Resisting Statistical Steganalysis Attacks: A Systematic Literature Review

Comments:

This article is a review article, so there is nothing new approach related to steganography techniques to be discussed. This article is well-written technically. And, although there have been many reviews of articles on image steganography, this article can still be considered because there is a special appreciation for the statistical approaches that are focused on this article.

However, there are important points that can be taken into consideration to improve the content of this article. In RQ2, the author tries to explore steganography techniques and methods that are currently widely used to deal with attacks. It would be good if the discussion of RQ2 also discussed the issues that are generally raised in research on steganography. So, it will be seen that these techniques/methods are proposed to overcome certain problems.

The connection between the problem/issue and the technique/method becomes clearer before discussing the advantages/weaknesses of a method. In short, if a 'problem/issue' column could be inserted before the 'technique/method' column, it seems that Table 7 would be more useful.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

**********

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10.1371/journal.pone.0308807.r002
Author response to Decision Letter 0
Submission Version1
2 Jul 2024

Editor Comments:

Comments1: The article should be subjected to a comprehensive review. Given that the article is a review study, it would be beneficial to include additional studies. The article should be updated in light of the suggestions provided by the referees. Some sections that are not essential should be shortened.

Response: We have comprehensively reviewed the article as per the suggestions. We have also included some additional studies, in line with suggestions of reviewers and the judgement of the editor, even though we believed our work contained enough recent studies on the subject. Some sections which we considered not essential have been reduced and some figures taken out. This can be seen in the tracked change revised manuscript. A total of 20 additional references were included as per reviewers’ recommendations.

We, however, want to bring to the attention of the editor, on what we term as Citation Forum Shopping as a consequence of the suggested articles to us by the reviewers. We respect the need for anonymity in academic peer reviewing. However, based on reference suggestions by some of the reviewers, which they termed recent publication, which are not anyway, Solak and Gutub could easily be identified as potential reviewers of our work. While we respect their expertise in the field, there are many more diverse researchers in this area, and we were surprised that all articles suggested had one particular name running through it. This may not be ethical, and we feel we must draw the attention of the editor to avoid such practices from reviewers in the Future. We do respect and value the high reputation of PLOS ONE in academic publication. Of course, most of the papers were not cited by us, as we did not consider most of them relevant to our work based on our own quality criteria. Those that we feel align to the objectives of our review were considered, though a handful as some of the suggested articles bear no semblance of our work. In the future, article suggestions from reviewers could be checked to avoid a situation where reviewers may want to take advantage of peer reviewing to force their papers on authors for purposes of citation counts.

See Paper Suggestions by Reviewer 1.

1. Solak S, Tezcan G. A New Dual Image Based Reversible Data Hiding Method Using Most Significant Bits and Center Shifting Technique. Applied Sciences. 2022 Oct 28;12(21):10933.

2. Abdirashid AM, Solak S, Sahu AK. Data Hiding Based on Frequency Domain Image Steganography. Avrupa Bilim ve Teknoloji Dergisi. 2022(42):71-6.

3. Solak S. High embedding capacity data hiding technique based on EMSD and LSB substitution algorithms. IEEE Access. 2020 Sep 10;8:166513-24.

4. Solak S, Altınışık U. Image steganography based on LSB substitution and encryption method: adaptive LSB+ 3. Journal of Electronic Imaging. 2019 Jul 1;28(4):043025-.

See Paper Suggestions by Reviewer 3

1. Aljarf A, Zamzami H, Gutub A. Integrating machine learning and features extraction for practical reliable color images steganalysis classification. Soft Computing. 2023 Oct;27(19):13877-88.

2. Hemalatha J, Sekar M, Kumar C, Gutub A, Sahu AK. Towards improving the performance of blind image steganalyzer using third-order SPAM features and ensemble classifier. Journal of Information Security and Applications. 2023 Aug 1;76:103541.

3. Aljarf A, Zamzami H, Gutub A. Is blind image steganalysis practical using feature-based classification?. Multimedia Tools and Applications. 2024 Jan;83(2):4579-612.

4. Hassan FS, Gutub A. Efficient reversible data hiding multimedia technique based on smart image interpolation. Multimedia Tools and Applications. 2020 Oct;79(39):30087-109.

5. Hassan FS, Gutub A. Novel embedding secrecy within images utilizing an improved interpolation-based reversible data hiding scheme. Journal of King Saud University-Computer and Information Sciences. 2022 May 1;34(5):2017-30.

6. Gutub A, Al-Shaarani F. Efficient implementation of multi-image secret hiding based on LSB and DWT steganography comparisons. Arabian Journal for Science and Engineering. 2020 Apr;45(4):2631-44.

7. Saeidi Z, Yazdi A, Mashhadi S, Hadian M, Gutub A. High performance image steganography integrating IWT and Hamming code within secret sharing. IET Image Processing. 2024 Jan;18(1):129-39.

8. Gutub A. Dynamic smart random preference for higher medical image confidentiality. Journal of Engineering Research. 2023;11(3).

9. Parvez MT, Gutub AA. Vibrant color image steganography using channel differences and secret data distribution. Kuwait J Sci Eng. 2011 Jun 1;38(1B):127-42.

10. Gutub AA. Pixel indicator technique for RGB image steganography. Journal of emerging technologies in web intelligence. 2010 Feb;2(1):56-64.

11. Sahu AK, Gutub A. Improving grayscale steganography to protect personal information disclosure within hotel services. Multimedia Tools and Applications. 2022 Sep;81(21):30663-83.

12. Hassan FS, Gutub A. Improving data hiding within colour images using hue component of HSV colour space. CAAI Transactions on Intelligence Technology. 2022 Mar;7(1):56-68.

13. Hassan FS, Gutub A. Efficient image reversible data hiding technique based on interpolation optimization. Arabian Journal for Science and Engineering. 2021 Sep;46(9):8441-56.

14. Al-Roithy B, Gutub A. Trustworthy image security via involving binary and chaotic gravitational searching within PRNG selections. Int. J. Comput. Sci. Netw. Secur. 2020 Dec;20(12):167-76.

15. Hureib ES, Gutub AA. Enhancing medical data security via combining elliptic curve cryptography and image steganography. Int. J. Comput. Sci. Netw. Secur.(IJCSNS). 2020 Aug;20(8):1-8.

16. Samkari H, Gutub A. Protecting medical records against cybercrimes within Hajj period by 3-layer security. Recent Trends Inf Technol Appl. 2019 Nov 15;2(3):1-21.

17. Hureib ES, Gutub AA. Enhancing medical data security via combining elliptic curve cryptography with 1-LSB and 2-LSB image steganography. International J Comp Sci Network Security (IJCSNS). 2020 Dec;20(12):232-41.

18. Samkari H, Gutub A. Protecting medical records against cybercrimes within Hajj period by 3-layer security. Recent Trends Inf Technol Appl. 2019 Nov 15;2(3):1-21.

19. Al-Shaarani F, Gutub A. Securing matrix counting-based secret-sharing involving crypto steganography. Journal of King Saud University-Computer and Information Sciences. 2022 Oct 1;34(9):6909-24.

20. Gutub A. Watermarking images via counting-based secret sharing for lightweight semi-complete authentication. International Journal of Information Security and Privacy (IJISP). 2022 Jan 1;16(1):1-8.

21. Al-Shaarani F, Gutub A. Increasing participants using counting-based secret sharing via involving matrices and practical steganography. Arabian Journal for Science and Engineering. 2022 Feb;47(2):2455-77.

22. AlKhodaidi T, Gutub A. Refining image steganography distribution for higher security multimedia counting-based secret-sharing. Multimedia Tools and Applications. 2021 Jan;80:1143-73.

23. Al-Juaid NA, Gutub AA, Khan EA. Enhancing PC data security via combining RSA cryptography and video based steganography. Journal of Information Security and Cybercrimes Research. 2018 Apr 17;1(1):5-13.

24. Alsaidi A, Al-lehaibi K, Alzahrani H, AlGhamdi M, Gutub A. Compression multi-level crypto stego security of texts utilizing colored email forwarding. Journal of Computer Science & Computational Mathematics (JCSCM). 2018 Sep;8(3):33-42.

25. Alassaf N, Gutub A, Parah SA, Al Ghamdi M. Enhancing speed of SIMON: A light-weight-cryptographic algorithm for IoT applications. Multimedia Tools and Applications. 2019 Dec;78:32633-57.

Reviewer #1:

Given that the comments were not numbered, we have decided to group the comments for easy response.

Comment 1: The importance of the article and its contribution to the literature are not reflected in the abstract. The abstract should include the context or background information for your research; the general topic under study; the specific topic of your research; why is it important to address these questions; the significance or implications of your findings or arguments. It must also contain numeric values. Please highlight your contribution. Reorganize the abstract to conclude:

(a) The overall purpose of the study and the research problems you investigated.

(b) The basic design of the study.

(c) Major findings or trends found as a result of the study.

(d) A brief summary of your interpretations and conclusions.

Response: We believe this comment does not reflect the information contained in our paper. The abstract has all relevant information, including the background, summary of interpretations and conclusions drawn from our review. We must indicate that, our abstract is in line with the PRISMA 2020 check list for systematic review, which is a mandatory requirement by PLOS ONE. We tailored our abstract to fulfil PLOS ONE requirements in the checklist.

Comments 2: Add more recent reference to enhance introduction section. Discuss the state-of-art techniques with their merits and issues. The literature should be developed and, if possible, presented in more papers published in last years. Discuss the research gaps and relate how the proposed work has improved them. In the introduction section, include spatial domain methods in your article. Also mention the difference between frequency and spatial domain methods For examples, “High embedding capacity data hiding technique based on EMSD and LSB substitution algorithms”, “Image steganography based on LSB substitution and encryption method: adaptive LSB+ 3”, “A New Dual Image Based Reversible Data Hiding Method Using Most Significant Bits and Center Shifting Technique” and “Data Hiding Based on Frequency Domain Image Steganography”

Response: The issues raised have been well discussed in our background literature. Spatial domain, frequency domain and Adaptive domain have all been adequately addressed. Please refer section 2 on background literature. Nonetheless, we have improved our introduction and included two of the suggested references.

Comments 3: You should submit more experimental study results for your work. You should also provide comparisons with similar studies.

What solution you propose to make the system more robust. What is your difference from similar studies?Prepare and explain your numerical examples more clearly for data hiding and data extraction processes.In the study, it is necessary to present complexity analysis and security analysis. For security tests, histogram analysis and PDH can be given.You must also provide numeric values. Rewrite the conclusion with following comments:(a) Highlight your analysis and reflect only the important points for the whole paper. (b) Mention the implication in the last of this section. Please, carefully review the manuscript to resolve these issues. (c) This section should be supported with numerical values. There are grammatical errors in the article.

Response: We want to believe that Reviewer 1 may have misconstrued our paper to be an original image steganography experiment work. Our work is a systematic review, and we did not conduct primary experiment. Most of the things being requested are things we do for primary steganography applications. We cannot do Complexity and Steganalysis security tests. These can only be done by original authors of the papers we used. Where the authors performed such tests, we reported, where they did not, we cannot do anything about them.

Reviewer #2:

Comment 1: The abstract contains more information than it should, for example, the sites that have been approved for papers and future work for the research presented. It is best to approach this matter in summary.

Response: As a systematic review article, we have to strictly follow the PLOS ONE 2020 PRISMA checklist for systematic review abstract. It is the reason why, it appears more information are included than necessary. This is a framework requirement. We have however made some grammatical corrections in the abstract.

Comment 2: Please provide the researcher's contributions in point form

Response: This has been done in the revised manuscript. Contribution has been written in point form as suggested by the reviewer.

Comment 3: The equations listed are not referenced to the references on which they were based. In addition, it would have been better to include performance evaluation metrics in a table showing each measure and its equation

Response: All equations have been referenced as suggested, although the source of each equations were already mentioned in the paper. We tried to put the equations in a table form but the presentation was not good for the flow of the work. We therefore left it as it was.

Comment 4: Is it possible to include a figure showing the superiority of the methods mentioned in Table 9 over other methods listed in the same table to demonstrate the efficiency of each method through the approved standards?

Response: We have included Figure 7 to comply with this suggested comment

Comment 5: Is it possible to arrange the references in Table 9 according to the year of publication for ease of follow-up?

Response: We have implemented this as suggested. Table 7,8, and 9 have been arranged in that order.

Comment 6: In Table 10, please add a field that shows the ratio of the number of images used to the maximum number of images used and discuss this matter due to its importance. I find it best to separate the table into two tables, the first for grayscale images and the other for color images for easy follow-up.

Response: We have separated table 10 into two for grayscale and color images as suggested. We now have table 10 and table 11. The ratio of the number of images used to the maximum number of images used is called the capacity, which is already part of our performance evaluation metrics. These results are reported in table 7,8, and 9 in the last column. Capacity is measured in many ways, some use ratio, others used capacity as a percentage whereas some studies used bit rate (BPP). We have reported depending on what the authors used. Please refer to the tables mentioned.

Comment 7: Rewriting in point form of Section 4.6 Future scope and research directions for image steganography

Response: We have done this as suggested. Please refer to the revised manuscript.

Comment 8: The research still needs to include other research that uses biometric techniques to hide images, such as voice or facial features. Please add such research to existing references

Response: We have included these techniques in our explanations as suggested. See section 2 for background literature and references 59-63.

Reviewer #3:

Comment 1- Give more elaboration on the real need for utilizing this Generative Adversarial Networks stego approaches. What is wrong in the normal other related stego methods requiring this kind of complex research. Try to support your explanation via real-life examples.

Response: These comments have been resolved in the revised manuscript. Explanations have been provided as suggested. Please refer to section 5, subsection 5.2 for the revision on the comments.

Comment 2- The study is recommended to consider the following image steganalysis researches within its coverage:

== "Integrating machine learning and features extraction for practical reliable color images steganalysis classification", Soft Computing 27(19):13877-13888 (2023)

== "Towards improving the performance of blind image steganalyzer using third-order SPAM features and ensemble classifier", Journal of Information Security and Applications 76:10354, Elsevier (2023)

== “Is blind image steganalysis practical using feature-based classification?”. Multimedia Tools and Applications (MTAP) 83(2): 4579–4612 (2024)

Response: The above articles have been referenced as we consider relevant to our work particularly on the background literature. Check reference 74-76.

Comment 3: The image security review presented is very promising showing its high capacity trade-off to security and robustness improvement as challenging processes. Try to benefit from the image stego secrecy researches below connecting to the capacity

Attachment Submitted filename: Rebuttal Letter to Editor.docx

10.1371/journal.pone.0308807.r003
Decision Letter 1
Solak Serdar Academic Editor
© 2024 Serdar Solak
2024
Serdar Solak
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
16 Jul 2024

PONE-D-24-14917R1Image Steganography Techniques for Resisting Statistical Steganalysis Attacks: A Systematic Literature ReviewPLOS ONE

Dear Dr. APAU,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

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Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Authors should reflect the importance of the article in the abstract. The abstract is too long, please review and shorten it. Although 125 references are also mentioned in the abstract, there are 147 items in the references section.

Use more academic language, have the article checked for grammar.

Check your section numbers.

Review your tables, figures and equations again.

You should increase the figure quality.

Check your fonts and size.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: (No Response)

Reviewer #3: Thanks for the hard work. The revision is performed perfect. All comments are addressed in satisfying condition.

Reviewer #4: Authors have revised and completed the manuscript according to my review from the previous round, especially to emphasize the RQ2 in their surveys.

The discussion about RQ2 is now more complete with an exploration of methods along with the relevant problems. Additional information in Tables 7, 8, and 9 as well as the given brief explanation can enrich the reader's knowledge, especially on research topics in image hiding and steganography fields.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Attachment Submitted filename: 2nd-comments.pdf

10.1371/journal.pone.0308807.r004
Author response to Decision Letter 1
Submission Version2
24 Jul 2024

Editor Comments:

Comment 1: Authors should reflect the importance of the article in the abstract.

Response: We have included in the abstract the main aim of the article and its importance to image steganography researchers and data protection practitioners.

Comment 2: The abstract is too long, please review and shorten it.

Response: This has been addressed. We have reviewed and shortened the abstract from the initial 300 words to 250 words.

Comment 3: Although 125 references are also mentioned in the abstract, there are 147 items in the references section.

Response: There is a difference between the 125 articles referenced in the abstract and the reference list generated at the end of the article. The 125 mentioned in the abstract is the total number of articles used to perform the systematic literature review. All those articles have been added at the end as APPENDIX I (RS1-RS125). This is separate from works cited in the paper which is the reference list of 148.

Comment 4: Use more academic language, have the article checked for grammar.

Response: we have performed academic language review and corrected where necessary. We have also used software including Grammarly to correct identified grammatical errors.

Comment 5: Check your section numbers.

Response: Ce have checked our section numbers. They are all sequential and correctly numbered.

Comment 6: Review your tables, figures and equations again.

Response: Table, figures and equations have been reviewed again to ensure they are correctly labelled and titled.

Comment 7: You should increase the figure quality.

Response: Figures whose quality was deemed low have been regenerated to ensure high resolution.

Comment 8: Check your fonts and size.

Response: Font size throughout the article have been set to times new roman 11 as per PLOS ONE specifications, except in tables where a lower font size is chosen to fit the words.

Comments by REVIEWERS:

We noticed that all reviewers accepted the revised manuscript as fully meeting their review requirements without further review. All reviewers appreciated the revised manuscript.

Attachment Submitted filename: Second Rebuttal Letter to Editor.docx

10.1371/journal.pone.0308807.r005
Decision Letter 2
Solak Serdar Academic Editor
© 2024 Serdar Solak
2024
Serdar Solak
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version2
26 Jul 2024

Image Steganography Techniques for Resisting Statistical Steganalysis Attacks: A Systematic Literature Review

PONE-D-24-14917R2

Dear Dr. APAU,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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After carefully reviewing the revised manuscript titled "Image Steganography Techniques for Resisting Statistical Steganalysis Attacks: A Systematic Literature Review" and considering the authors' responses to the reviewers' and editor's suggestions, I find that the authors have made the necessary revisions to address all concerns raised.

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10.1371/journal.pone.0308807.r006
Acceptance letter
Solak Serdar Academic Editor
© 2024 Serdar Solak
2024
Serdar Solak
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
1 Aug 2024

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References

1 Apau R. , Koranteng F. N. , and Gyamfi S. A , “Cyber-crime and its effects on E-commerce technologies,” Journal of Information, vol. 5 , no. 1 , pp. 39–59, Aug. 2019, doi: 10.18488/journal.104.2019.51.39.59
2 Park H. S. , “Technology convergence, open innovation, and dynamic economy,” Journal of Open Innovation: Technology, Market, and Complexity 2017, vol. 3 , no. 4 , p. 24, Nov. 2017, doi: 10.1186/S40852-017-0074-Z
3 Aslam S. , “80+ Facebook Statistics You Need to Know in 2023—Omnicore.” Accessed: Jun. 18, 2023. [Online]. Available: https://www.omnicoreagency.com/facebook-statistics/.
4 Chewning L. V. , Lai C. H. , and Doerfel M. L. , “Organizational Resilience and Using Information and Communication Technologies to Rebuild Communication Structures,” Management Communication Quarterly, vol. 27 , no. 2 , pp. 237–263, May. 2013, doi: 10.1177/0893318912465815
5 Waseso B. M. P. , and Setiyanto N. A. , “Web phishing classification using combined machine learning methods,” Journal of Computing Theories and Applications, vol. 1 , no. 1 , pp. 11–18, Aug. 2023, doi: 10.33633/jcta.v1i1.8898
6 Seh A. H. , Zarour M. , Alenezi M. , Sarkar A. K. , Agrawal A. , Kumar R. , et al , “Healthcare Data Breaches: Insights and Implications,” Healthcare, vol. 8 , no. 2 , p. 133, May 2020, doi: 10.3390/healthcare8020133 32414183
7 Wong W. P. , Tan H. C. , Tan K. H. and Tseng M. L. , “Human factors in information leakage: mitigation strategies for information sharing integrity,” Industrial Management and Data Systems, vol. 119 , no. 6 , pp. 1242–1267, Aug. 2019, doi: 10.1108/IMDS-12-2018-0546/FULL/PDF
8 Apau R. , and Koranteng F. N. , “Impact of Cybercrime and Trust on the Use of E-Commerce Technologies: An Application of the Theory of Planned Behavior,” International Journal of Cyber Criminology, vol 13 , no. 2 , pp. 228–254, Jul. 2020, doi: 10.5281/zenodo.3697886
9 Ventures Cybersecurity , “Cybercrime To Cost The World $10.5 Trillion Annually By 2025,” Cybercrime Magazine, Sausalito, Nov. 2020. [Online]. Available: https://cybersecurityventures.com/hackerpocalypse-cybercrime-report-2016/ (Accessed 11 April 2024).
10 Song V. , “Mother of All Breaches Exposes 773 Million Emails, 21 Million Passwords,” Gizmodo, 2019. Accessed: Feb. 2 , 2023. [Online]. Available at https://gizmodo.com/mother-of-all-breaches-exposes-773-million-emails-21-m-1831833456.
11 Thales C.P. , “2022 Thales Data Threat Report.” 2022. Accessed: May 16, 2023. [Online]. Available at https://cpl.thalesgroup.com/2022/data-threat-report.
12 Drapkin A. , “Data Breaches That Have Happened in 2022 and 2023 So Far,” Tech.co, 2023. Accessed: Feb 2, 2023. [Online]. Available at https://tech.co/news/data-breaches-updated-list.
13 Nallainathan S. , “Analysis onto the Evolving Cyber-Attack Trends during COVID-19 Pandemic,” International Journal of Science and Research, vol. 10 , no. 4 , pp. 1–6, Apr. 2021, doi: 10.21275/SR21403140109
14 Bouveret A. , “Cyber Risk for the Financial Sector: A Framework for Quantitative Assessment,” IMF Working Papers, vol. 2018 , no. 143 , Jun. 2018, doi: 10.5089/9781484360750.001.A001
15 Amsaveni A. , and Vanathi P. T. , “A comprehensive study on image steganography and steganalysis techniques,” International Journal of Information and Communication Technology, vol. 7 , no. 4–5 , pp. 406–424, Jul. 2015, doi: 10.1504/IJICT.2015.070300
16 Qadir A. M. , and Varol N. , “A review paper on cryptography,” 7th International Symposium on Digital Forensics and Security, ISDFS 2019, pp. 1–6, Jun. 2019, doi: 10.1109/ISDFS.2019.8757514
17 Wu Z. , Guo J. , Zhang C. , and Li C. , “Steganography and Steganalysis in Voice over IP: A Review,” Sensors 2021, vol. 21 , no. 4 , p. 1032, Feb. 2021, doi: 10.3390/s21041032 33546240
18 Sahu A. K. and Sahu M. , “Digital image steganography and steganalysis: A journey of the past three decades,” Open Computer Science, vol. 10 , no. 1 , pp. 296–342, Jan. 2020, doi: 10.1515/COMP-2020-0136/XML
19 Naveenkumar R. , Sivamangai N. M. , Napolean A. , and Sridevi Sathyapriya S. , “Enhancing Encryption Security Against Cypher Attacks,” Homomorphic Encryption for Financial Cryptography, pp. 125–155, Aug. 2023, Springer, Cham, doi: 10.1007/978-3-031-35535-6_7
20 Stallings W. and Brown L. , Computer security: principles and practice. 2017. Fourth Edition. Upper Saddle River: Pearson Education.
21 El Mrabet N. and Joye M. , “Guide to pairing based cryptography,” 2017, Accessed: Sep. 22, 2023. [Online]. Available: https://hal.science/hal-01579628.
22 Girdhar A. and Kumar V. , “Comprehensive survey of 3D image steganography techniques,” IET Image Process, vol. 12 , no. 1 , pp. 1–10, Jan. 2018, doi: 10.1049/IET-IPR.2017.0162
23 Khalid M. , Rahmani I. , Arora K. , Pal N. and Scholar M. T. , “A Crypto-Steganography: A Survey,” IJACSA) International Journal of Advanced Computer Science and Applications, vol. 5 , no. 7 , pp. 149–155, May. 2014, 6d993a6490fe999c72e04d57bf5422db8883f8d1.
24 Mahto D. and Yadav D.K. , “RSA and ECC: A Comparative Analysis,” International Journal of Applied Engineering Research, vol. 12 , pp. 9053–9061, Oct. 2017.
25 Pramanik S. , Bandyopadhyay S. K. , and Ghosh R. , “Signature Image Hiding in Color Image using Steganography and Cryptography based on Digital Signature Concepts,” 2nd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2020—Conference Proceedings, pp. 665–669, Mar. 2020, doi: 10.1109/ICIMIA48430.2020.9074957
26 Rasras R. , Alqadi Z. , Sara M.A. , Rasras R. J. , Alqadi Z. A. , Rasmi M. , et al , “A Methodology Based on Steganography and Cryptography to Protect Highly Secure Messages,” Technology & Applied Science Research, vol. 9 , no. 1 , pp. 3681–3684, Feb. 2019, doi: 10.48084/etasr.2380
27 Din R. , Utama S. , and Mustapha A. , “Evaluation Review on Effectiveness and Security Performances of Text Steganography Technique,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 11 , no. 2 , pp. 747–754, Aug. 2018, doi: 10.11591/ijeecs.v11.i2.pp747-754
28 Poornima R. and Iswarya R. J. , “An overview of digital image steganography,” International Journal of Computer Science & Engineering Survey (IJCSES), vol. 4 , no. 1 , pp. 23–31, Feb. 2013, doi: 10.5121/ijcses.2013.4102
29 Lou D. C. , and Hu C. H. , “LSB steganographic method based on reversible histogram transformation function for resisting statistical steganalysis,” Inf Sci (N Y), vol. 188 , pp. 346–358, Apr. 2012, doi: 10.1016/J.INS.2011.06.003
30 Bhattacharyya D. and Banerjee D. I. , “A Survey of Steganography and Steganalysis Technique in Image, Text, Audio and Video as Cover Carrier CFC: Big Data Security View project Cloud computing View project,” Journal of global research in computer science, vol. 2 , no. 4 , pp. 1–16, May. 2011.
31 Febryan A. , Purboyo T. W. , & Saputra R. E. , “Steganography Methods on Text, Audio, Image and Video: A Survey,” International Journal of Applied Engineering Research, vol. 12 , no. 21 , pp. 10485–10490, Apr. 2017.
32 Chanu Y. J. , Singh K. M. , & Tuithung T. , “Image steganography and steganalysis: A survey,” International Journal of Computer Applications, vol. 52 , no. 2 , pp. 975–8887, Aug. 2012.
33 Kaur M. and Kaur G. , “Review of Various Steganalysis Techniques,” International journal of computer science and information technologies, vol. 5 , no. 2 , pp. 1744–1747, Jan. 2014.
34 Shehab D. A. and Alhaddad M. J. , “Comprehensive Survey of Multimedia Steganalysis: Techniques, Evaluations, and Trends in Future Research,” Symmetry 2022, vol. 14 , no. 1 , p. 117, Jan. 2022, doi: 10.3390/SYM14010117
35 Tasheva A. , Tasheva Z. , and Nakov P. , “Image based steganography using modified LSB insertion method with contrast stretching,” ACM International Conference Proceeding Series, vol. Part F132086, pp. 233–240, Jun. 2017, doi: 10.1145/3134302.3134325
36 Shelke F. M. , Dongre A. A. , and Soni P. D. , “Comparison of different techniques for Steganography in images,” International Journal of Application or Innovation in Engineering & Management, vol. 3, no. 2, 3 (2 ), pp.171–176, Feb. 2014.
37 Apau R. and Adomako C. , “Design of Image Steganography based on RSA Algorithm and LSB Insertion for Android Smartphones,” Int J Comput Appl, vol. 164 , no. 1 , pp. 13–22, Apr. 2017, doi: 10.5120/ijca2017913557
38 Zhang X. , Peng F. , and Long M. , “Robust Coverless Image Steganography Based on DCT and LDA Topic Classification,” IEEE Trans Multimedia, vol. 20 , no. 12 , pp. 3223–3238, Dec. 2018, doi: 10.1109/TMM.2018.2838334
39 Liu T. Y. and Tsai W. H. , “A new steganographic method for data hiding in microsoft word documents by a change tracking technique,” IEEE Transactions on Information Forensics and Security, vol. 2 , no. 1 , pp. 24–30, Mar. 2007, doi: 10.1109/TIFS.2006.890310
40 Deshmukh P. V. , Kapse A. S. , Thakare V. M. , and Kapse A. S. , “High-capacity reversible data hiding in encrypted images using multi-MSB data hiding mechanism with elliptic curve cryptography,” Multimedia Tools and Applications, vol. 82 , no. 18 , pp. 28087–28115, Jul. 2023, doi: 10.1007/S11042-023-14683-9/FIGURES/17
41 Laskar S. A. and Hemachandran K. , “A Review on Image Steganalysis techniques for attacking Steganography,” International Journal of Engineering Research and Technology, vol. 3 , no. 1 , pp. 3400–3410, Jan. 2014, doi: IJERTV3IS11136
42 Kaur M. , Kumar V. , and Singh D. , “An efficient image steganography method using multiobjective differential evolution,” Digital Media Steganography: Principles, Algorithms, and Advances, pp. 65–79, Jan. 2020, doi: 10.1016/B978-0-12-819438-6.00012–8
43 Luo W. , Huang F. , and Huang J. , “Edge adaptive image steganography based on lsb matching revisited,” IEEE Transactions on Information Forensics and Security, vol. 5 , no. 2 , pp. 201–214, Jun. 2010, doi: 10.1109/TIFS.2010.2041812
44 Chhabra A. , Woeden T. , Singh D. , Rakhra M. , Dahiya O. , and Gupta A. , “Image Steganalysis with Image decoder using LSB and MSB Technique,” Proceedings of 3rd International Conference on Intelligent Engineering and Management, ICIEM 2022, pp. 900–905, Apr. 2022, doi: 10.1109/ICIEM54221.2022.9853039
45 AlFaqawi L. , AbuHaya M. , & Barhoom T. , “Alpha channel-Based Indicator for Robustness Forward/Backward LSB Steganography,” Proceedings—2021 Palestinian International Conference on Information and Communication Technology, PICICT 2021, pp. 163–168, Sep. 2021, doi: 10.1109/PICICT53635.2021.00039
46 Braci S. , Delpha C. and Boyer R. , “How quantization based schemes can be used in image steganographic context,” Signal Processing: Image Communication, vol. 26 , no. 8–9 , pp. 567–576, Oct. 2011, doi: 10.1016/J.IMAGE.2011.07.006
47 Hong W. , Chen T. S. , and Luo C. W. , “Data embedding using pixel value differencing and diamond encoding with multiple-base notational system,” Journal of Systems and Software, vol. 85 , no. 5 , pp. 1166–1175, May. 2012, doi: 10.1016/J.JSS.2011.12.045
48 Wu D. C. and Tsai W. H. , “A steganographic method for images by pixel-value differencing,” Pattern Recognition Letters, vol. 24 , no. 9–10 , pp. 1613–1626, Jun. 2003, doi: 10.1016/S0167-8655(02)00402-6
49 Zhang X. and Wang S. , “Steganography using multiple-base notational system and human vision sensitivity,” IEEE Signal Processing Letters, vol. 12 , no. 1 , pp. 67–70, Jan. 2005, doi: 10.1109/LSP.2004.838214
50 Kaur S. , Singh S. , Kaur M. , and Lee H. N. , “A Systematic Review of Computational Image Steganography Approaches,” Archives of Computational Methods in Engineering, vol. 29 , no. 7 , pp. 4775–4797, Nov. 2022, doi: 10.1007/S11831-022-09749-0/TABLES/4
51 Solak S. and Tezcan G. A “New Dual Image Based Reversible Data Hiding Method Using Most Significant Bits and Center Shifting Technique.,” Applied Sciences, vol. 12 , no. 21 , p. 10933, Oct. 2022, doi: 10.3390/app122110933
52 Abdirashid A. M. , Solak S. , & Sahu A. K. (2022). Data Hiding Based on Frequency Domain Image Steganography. European Journal of Science and Technology, vol. 42 , pp. 71–76, Oct. 2022, doi: 10.31590/ejosat.1188597
53 Chen W. Y. , “Color image steganography scheme using set partitioning in hierarchical trees coding, digital Fourier transform and adaptive phase modulation,” Applied Mathematics and Computation, vol. 185 , no. 1 , pp. 432–448, Feb. 2007, doi: 10.1016/J.AMC.2006.07.041
54 Hassaballah M. , Hameed M. A. , Awad A. I. , & Muhammad K. , “A Novel Image Steganography Method for Industrial Internet of Things Security,” IEEE Transactions on Industrial Informatics, vol. 17 , no. 11 , pp. 7743–7751, Nov. 2021, doi: 10.1109/TII.2021.3053595
55 Narasimmalou T. and Allen J. R. , “Optimized discrete wavelet transform based steganography,” Proceedings of 2012 IEEE International Conference on Advanced Communication Control and Computing Technologies, ICACCCT 2012, pp. 88–91, Aug. 2012, doi: 10.1109/ICACCCT.2012.6320747
56 Huang F. , Huang J. , and Shi Y. Q. , “New channel selection rule for JPEG steganography,” IEEE Transactions on Information Forensics and Security, vol. 7 , no. 4 , pp. 1181–1191, May. 2012, doi: 10.1109/TIFS.2012.2198213
57 Apau R. , Hayfron-Acquah J. B. , & Twum F. , “Enhancing data security using video steganography, RSA and Huffman code algorithms with LSB insertion,” International Journal of Computer Applications, vol. 143 , no. 4 , pp. 975–8887, Sep. 2016, doi: 10.5120/ijca2016910156
58 Kaur A. , Kaur R. , and Kumar N. , “A Review on Image Steganography Techniques,” International Journal of Computer Applications, vol. 123 , no. 4 , pp. 975–8887, Jan. 2015.
59 Karthika P. , Babu R. G , and Jayaram K. , “Biometric based on steganography image security in wireless sensor networks,” Procedia Computer Science, vol. 67 , pp. 1291–1299, Jan. 2020, doi: 10.1016/j.procs.2020.03.445
60 Oduguwa T. and Arabo A. , “Passwordless Authentication Using a Combination of Cryptography, Steganography, and Biometrics,” Journal of Cybersecurity and Privacy, vol. 4 , no. 2 , pp. 278–297, May. 2024, doi: 10.3390/jcp4020014
61 Bernal-Romero J. C , Ramirez-Cortes J. M , Rangel-Magdaleno J. D , Gomez-Gil P , Peregrina-Barreto H , and Cruz-Vega I. , “A review on protection and cancelable techniques in biometric systems,” IEEE Access, vol. 11 , pp. 8531–8568, Jan. 2023, doi: 10.1109/ACCESS.2023.3239387
62 Lu S. Y. , and Lin C. Y. , “A Robust Coverless Image Steganography Method Based on Face Recognition and Camouflage Image,” In 2024 4th Asia Conference on Information Engineering (ACIE), pp. 52–57, Jan. 2024, doi: 10.1109/ACIE61839.2024.00016
63 Kumar V. , and Sharma S. , “Steganography-based facial re-enactment using generative adversarial networks,” Multimedia Tools and Applications, vol. 83 , no. 3 , pp. 7609–7630, Jan. 2024, doi: 10.1007/s11042-023-15946-1
64 Juarez-Sandoval O. , Cedillo-Hernandez M. , Sanchez-Perez G. , Toscano-Medina K. , Perez-Meana H. , & Nakano-Miyatake M. , “Compact image steganalysis for LSB-matching steganography,” Proceedings—2017 5th International Workshop on Biometrics and Forensics, IWBF 2017, May 2017, doi: 10.1109/IWBF.2017.7935103
65 Rathika L , Loganathan B , Scholar MP , Nadu T , and Nadu T ., “Approaches and methods for steganalysis–A survey,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 6 , no. 6 , pp. 433–438, Jun. 2017.
66 Westfeld A. and Pfitzmann A. , “Attacks on steganographic systems breaking the steganographic utilities ezstego, jsteg, steganos, and s-tools–and some lessons learned,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1768 , pp. 61–76, 2000, doi: 10.1007/10719724_5/COVER
67 Provos N. , “Defending Against Statistical Steganalysis,” in 10th USENIX Security Symposium (USENIX Security 01), pp. 323–336, 2001. Accessed: May 23, 2023. [Online]. Available at https://www.usenix.org/legacy/events/sec01/full_papers/provos/provos_html/.
68 Dumitrescu S. , Wu X. , and Wang Z. , “Detection of LSB steganography via sample pair analysis,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2578 , pp. 355–372, 2003, doi: 10.1007/3-540-36415-3_23/COVER
69 Fridrich J. , Goljan M. , and Du R. , “Reliable detection of LSB steganography in color and grayscale images,” Proceedings of the ACM International Multimedia Conference and Exhibition, no. II, pp. 27–30, Mar. 2001, doi: 10.1145/1232454.1232466
70 Apau R. , Hayfron-Acquah J.B. , Asante M. and Twum F. , “A Multilayered Secure Image Steganography Technique for Resisting Regular-Singular Steganalysis Attacks Using Elliptic Curve Cryptography and Genetic Algorithm” In International Conference on ICT for Sustainable Development, Vol. 754 , pp. 427–439, Aug. 2023, Springer Nature Singapore, doi: 10.1007/978-981-99-4932-8_39
71 Manoharan S. , Master A. , and Speidel U. , “Complexity-based steganalysis,” in 2014 International Symposium on Information Theory and its Applications, pp. 40–44, May. 2014.
72 Xia Z. , Wang X. , Sun X. , Liu Q. , & Xiong N. , “Steganalysis of LSB matching using differences between nonadjacent pixels,” Multimedia Tools Applications, vol. 75 , no. 4 , pp. 1947–1962, Feb. 2016, doi: 10.1007/S11042-014-2381-8/TABLES/2
73 Kulkarni Y. and Gorkar A. , “Intensive Image Malware Analysis and Least Significant Bit Matching Steganalysis,” Proceedings—2020 IEEE International Conference on Big Data, Big Data 2020, pp. 2309–2317, Dec. 2020, doi: 10.1109/BIGDATA50022.2020.9377974
74 Ahmed I. T. , Hammad B.T. , and Jamil N. , “Image Steganalysis based on Pretrained Convolutional Neural Networks,” 2022 IEEE 18th International Colloquium on Signal Processing and Applications, CSPA 2022—Proceeding, pp. 283–286, Apr. 2022, doi: 10.1109/CSPA55076.2022.9782061
75 Aljarf A. , Zamzami H. , and Gutub A. , “Integrating machine learning and features extraction for practical reliable color images steganalysis classification,” Soft Computing, vol. 27 , no. 19 , pp. 13877–13888, Oct. 2023, doi: 10.1007/s00500-023-09042-7
76 Hemalatha J. , Sekar M. , Kumar C. , Gutub A. , and Sahu A. K. , “Towards improving the performance of blind image steganalyzer using third-order SPAM features and ensemble classifier,” Journal of Information Security and Applications, vol. 76 , p.103541, Aug. 2023, doi: 10.1016/j.jisa.2023.103541
77 Aljarf A. , Zamzami H. , and Gutub A. , “Is blind image steganalysis practical using feature-based classification?,” Multimedia Tools and Applications, vol. 83 , no. 2 , 4579–4612, Jan. 2024, doi: 10.1007/s11042-023-15682-6
78 Ashwin P. , Wieczorek S. , Vitolo R. , and Cox P. , “Tipping points in open systems: bifurcation, noise-induced and rate-dependent examples in the climate system,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 370 , no. 1962 , pp. 1166–1184, Mar. 2012, doi: 10.1098/rsta.2011.0306 22291228
79 Subhedar M. S. and Mankar V. H. , “Current status and key issues in image steganography: A survey,” Computer Science Review, vol. 13–14 , no. C, pp. 95–113, Nov. 2014, doi: 10.1016/J.COSREV.2014.09.001
80 Kadhim I. J , Premaratne P. , Vial P. J. , and Halloran B. , “Comprehensive survey of image steganography: Techniques, Evaluations, and trends in future research,” Neurocomputing, vol. 335 , pp. 299–326, Mar. 2019, doi: 10.1016/J.NEUCOM.2018.06.075
81 Mandal P. C. , Mukherjee I. , and Chatterji B. N. , “High capacity steganography based on IWT using eight-way CVD and n-LSB ensuring secure communication,” Optik (Stuttg), vol. 247 , p. 167804, Dec. 2021, doi: 10.1016/J.IJLEO.2021.167804
82 Hussain M. , Wahab A. W. A. , Idris Y. I. B. , Ho A. T. , & Jung K. H. , “Image steganography in spatial domain: A survey,” Signal Processing: Image Communication, vol. 65 , pp. 46–66, Jul. 2018, doi: 10.1016/J.IMAGE.2018.03.012
83 Girdhar A. and Kumar V. , “Comprehensive survey of 3D image steganography techniques,” IET Image Processing, vol. 12 , no. 1 , pp. 1–10, Jan. 2018, doi: 10.1049/IET-IPR.2017.0162
84 Meng R. , Cui Q. , and Yuan C. , “A Survey of Image Information Hiding Algorithms Based on Deep Learning,” CMES—Computer Modeling in Engineering and Sciences, vol. 117 , no. 3 , pp. 425–454, 2018, doi: 10.31614/CMES.2018.04765
85 Qin J. , Luo Y. , Xiang X. , Tan Y. , & Huang H. , “Coverless image steganography: A survey,” IEEE Access, vol. 7 , pp. 171372–171394, Nov. 2019, doi: 10.1109/ACCESS.2019.2955452
86 Puteaux P. , Ong S. , Wong K. , & Puech W. , “A survey of reversible data hiding in encrypted images–The first 12 years,” Journal of Visual Commun Image Representation, vol. 77 , p. 103085, May 2021, doi: 10.1016/J.JVCIR.2021.103085
87 Aslam M. A. , Rashid M. , Azam F. , Abbas M. , Rasheed Y. , Alotaibi S. S. , et al , “Image Steganography using Least Significant Bit (LSB)-A Systematic Literature Review,” Proceedings of 2022 2nd International Conference on Computing and Information Technology, ICCIT 2022, pp. 32–38, 2022, doi: 10.1109/ICCIT52419.2022.9711628
88 Kitchenham B , and Charters S. , “Guidelines for performing Systematic Literature Reviews in Software Engineering,” 2007. Keele Univ., Univ. Durham, Durham, U.K., EBSE Tech. Rep. EBSE-2007. Accessed: Apr 21, 2023. [Online]. Availabel at https://www.researchgate.net/profile/Barbara-Kitchenham/publication/302924724_Guidelines_for_performing_Systematic_Literature_Reviews_in_Software_Engineering/links/61712932766c4a211c03a6f7/Guidelines-for-performing-Systematic-Literature-Reviews-in-Software-Engineering.pdf.
89 Mohsin A. H. , Zaidan A. A. , Zaidan B. B. , Albahri O. S. , Albahri A. S. , Alsalem M. A. , et al , “Blockchain authentication of network applications: Taxonomy, classification, capabilities, open challenges, motivations, recommendations and future directions,” Computer Standard and Interfaces, vol. 64 , pp. 41–60, May 2019, doi: 10.1016/J.CSI.2018.12.002
90 Harie Y. , Gautam B. P. , and Wasaki K. , “Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review,” Applied Sciences 2023, vol. 13 , no. 9 , p. 5335, Apr. 2023, doi: 10.3390/APP13095335
91 Alshehri F. and Muhammad G. , “A Comprehensive Survey of the Internet of Things (IoT) and AI-Based Smart Healthcare,” IEEE Access, vol. 9 , pp. 3660–3678, May. 2021, doi: 10.1109/ACCESS.2020.3047960
92 Page M. J. , McKenzie J. E. , Bossuyt P. M. , Boutron I. , Hoffmann T. C. , Mulrow C. D. , et al , “The PRISMA 2020 statement: An updated guideline for reporting systematic reviews,” International Journal of Surgery, vol. 88 , p. 105906, Apr. 2021, doi: 10.1016/J.IJSU.2021.105906 33789826
93 Brereton P. , Kitchenham B. A. , Budgen D. , Turner M. , and Khalil M , “Lessons from applying the systematic literature review process within the software engineering domain,” Journal of Systems and Software, vol. 80 , no. 4 , pp. 571–583, Apr. 2007, doi: 10.1016/J.JSS.2006.07.009
94 Basili V. R. , Caldiera G. , and Rombach H. D. , “The goal question metric approach,” Encyclopedia of Software Engineering, vol. 2 , pp. 528–532, Jan. 1994, doi: 10.1.1.104.8626
95 Lun Y. Z. , D’Innocenzo A. , Smarra F. , Malavolta I. , and Di Benedetto M. D. , “State of the art of cyber-physical systems security: An automatic control perspective,” Journal of Systems and Software, vol. 149 , pp. 174–216, Mar. 2019, doi: 10.1016/J.JSS.2018.12.006
96 Wiafe I. , Koranteng F. N. , Obeng E. N. , Assyne N. , Wiafe A. , & Gulliver S. R. , “Artificial Intelligence for Cybersecurity: A Systematic Mapping of Literature,” IEEE Access, vol. 8 , pp. 146598–146612, Oct. 2020, doi: 10.1109/access.2020.3013145
97 Sajasi S. , and Moghadam A. M. E. , “An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method,” Applied Soft Computing, vol. 30 , pp. 375–389, May 2015, doi: 10.1016/J.ASOC.2015.01.032
98 Chefranov A. G. and Öz G. , “Adaptive to pixel value and pixel value difference irreversible spatial data hiding method using modified LSB for grayscale images,” Journal of Information Security and Applications, vol. 70 , p. 103314, Nov. 2022, doi: 10.1016/J.JISA.2022.103314
99 Yadav G. S. and Ojha A. , “Hamiltonian path based image steganography scheme with improved imperceptibility and undetectability,” Appl Soft Comput, vol. 73 , pp. 497–507, Dec. 2018, doi: 10.1016/J.ASOC.2018.08.034
100 Apau R. and Gyamfi S. A. , “Data Hiding in Audio Signals using Elliptic Curve Cryptography, Huffman Code Algorithm and Low-Bit Encoding,” International Journal of Computer Applications, vol. 180 , no. 39 , pp. 24–34, May. 2018, doi: 10.5120/ijca2018917038
101 Hemalatha S. , Acharya U. D. , and Renuka A. , “Wavelet Transform Based Steganography Technique to Hide Audio Signals in Image,” Procedia Computer Science, vol. 47 , no. C, pp. 272–281, Jan. 2015, doi: 10.1016/J.PROCS.2015.03.207
102 Yadav S. , “Correlation analysis in biological studies,” Journal of the Practice of Cardiovascular Sciences, vol. 4 , no. 2 , p. 116, Jan. 2018, doi: 10.4103/JPCS.JPCS_31_18
103 Sun Y. and Liu F. , “Selecting cover for image steganography by correlation coefficient,” 2nd International Workshop on Education Technology and Computer Science, ETCS 2010, vol. 2 , pp. 159–162, 2010, doi: 10.1109/ETCS.2010.33
104 Kumar N. , Hoffmann N. , Oelschlägel M. , Koch E. , Kirsch M. , & Gumhold S. , “Structural Similarity Based Anatomical and Functional Brain Imaging Fusion,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11846 LNCS, pp. 121–129, 2019, doi: 10.1007/978-3-030-33226-6_14/TABLES/1
105 Tseng H. W. and Leng H. S. , “A reversible modified least significant bit (LSB) matching revisited method,” Signal Processing: Image Communication, vol. 101 , p. 116556, Feb. 2022, doi: 10.1016/J.IMAGE.2021.116556
106 Rustad S. , Andono P. N. , and Shidik G. F. , “Digital image steganography survey and investigation (goal, assessment, method, development, and dataset),” Signal Processing, vol. 206 , p. 108908, May 2023, doi: 10.1016/J.SIGPRO.2022.108908
107 Wang Y. , Wang L. , and Teo K. L. , “Necessary and Sufficient Optimality Conditions for Regular–Singular Stochastic Differential Games with Asymmetric Information,” Journal of Optimisation Theory and Applications, vol. 179 , no. 2 , pp. 501–532, Nov. 2018, doi: 10.1007/S10957-018-1251-3/METRICS
108 Kaur M. , Alzubi A. A. , Walia T. S. , Yadav V. , Kumar N. , Singh D. , et al , “EGCrypto: A Low-Complexity Elliptic Galois Cryptography Model for Secure Data Transmission in IoT,” IEEE Access, vol. 11 , pp. 90739–90748, Aug. 2023, doi: 10.1109/ACCESS.2023.3305271
109 Chuang Y. H. , Lin B. S. , Chen Y. X. , & Shiu H. J. , “Steganography in RGB Images Using Adjacent Mean,” IEEE Access, vol. 9 , pp. 164256–164274, Dec. 2021, doi: 10.1109/ACCESS.2021.3132424
110 Grime M. M. and Wright G. , “Delphi Method,” in Wiley StatsRef: Statistics Reference Online, Wiley, vol 1 . No. 16, pp. 1–6, 2016, doi: 10.1002/9781118445112.stat07879
111 Yadav G. S. , Mangal P. , Parmar G. , and Soliya S. , “Genetic algorithm and hamilton path based data hiding scheme including embedding cost optimization,” Multimedia Tools and Applications, vol. 82 , no. 13 , pp. 20233–20249, May 2023, doi: 10.1007/S11042-022-14322-9/TABLES/8
112 Stalin S. , Maheshwary P. , and Shukla P. K. , “Payback of image encryption techniques: A quantitative investigation,” 2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019, pp. 1370–1380, May 2019, doi: 10.1109/ICCS45141.2019.9065762
113 Basuki A. I. and Rosiyadi D. , “Joint Transaction-Image Steganography for High Capacity Covert Communication,” 2019 International Conference on Computer, Control, Informatics and its Applications: Emerging Trends in Big Data and Artificial Intelligence, IC3INA 2019, pp. 41–46, Oct. 2019, doi: 10.1109/IC3INA48034.2019.8949606
114 Gupta A. , Pramanik S. , Bui H. T. , and Ibenu N. M. , “Machine Learning and Deep Learning in Steganography and Steganalysis,” Multidisciplinary Approach to Modern Digital Steganography, pp. 75–98, Jan. 2021, doi: 10.4018/978-1-7998-7160-6.CH004
115 Singh S. , Siddiqui T. J. , and Siddiqui T. J. , “A Security Enhanced Robust Steganography Algorithm for Data Hiding,” International Journal of Computer Science Issues (IJCSI), vol. 9 , no. 3 , pp. 131–139, Sep. 2012.
116 Kaur R. and Singh B. , “A hybrid algorithm for robust image steganography,” Multidimensional Systems and Signal Processing, vol. 32 , no. 1 , pp. 1–23, Jan. 2021, doi: 10.1007/S11045-020-00725-0/TABLES/9
117 Chen X. , Kishore V. , Weinberger K. Q. , Chen X. , Kishore V. , and Weinberger K. Q. , “Learning Iterative Neural Optimizers for Image Steganography,” ArXiv, p. arXiv:2303.16206, Mar. 2023, doi: 10.48550/ARXIV.2303.16206
118 Yadav S. K. , Jha S. K. , Sharma U. K. , Shrama S. , Dixit P. , Prakash S. , et al , “An Efficient Security Technique Using Steganography and Machine Learning,” In: Goyal D. , Kumar A. , Piuri V. , Paprzycki M. (eds) Proceedings of the Third International Conference on Information Management and Machine Intelligence. Algorithms for Intelligent Systems. Springer, Singapore pp. 53–58, 2023, doi: 10.1007/978-981-19-2065-3_7
119 Bai J. , Chang C. C. , Nguyen T. S. , Zhu C. , & Liu Y. , “A high payload steganographic algorithm based on edge detection,” Displays, vol. 46 , pp. 42–51, Jan. 2017, doi: 10.1016/J.DISPLA.2016.12.004
120 Yuan H. D. , “Secret sharing with multi-cover adaptive steganography,” Inf Sci (N Y), vol. 254 , pp. 197–212, Jan. 2014, doi: 10.1016/J.INS.2013.08.012
121 Abdulla A. A. , Sellahewa H. , and Jassim S. A. , “Stego quality enhancement by message size reduction and fibonacci bit-plane mapping,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8893 , pp. 151–166, 2014, doi: 10.1007/978-3-319-14054-4_10/COVER
122 Al-Dmour H. and Al-Ani A. , “A steganography embedding method based on edge identification and XOR coding,” Expert Systems with Applications, vol. 46 , pp. 293–306, Mar. 2016, doi: 10.1016/J.ESWA.2015.10.024
123 Abikoye O. C. , Ojo U. A. , Awotunde J. B. , & Ogundokun R. O. , “A safe and secured iris template using steganography and cryptography,” Multimedia Tools and Applications, vol. 79 , no. 31–32 , pp. 23483–23506, Aug. 2020, doi: 10.1007/S11042-020-08971-X/TABLES/2
124 Nguyen T. D. , Arch-Int S. , & Arch-Int N. , “An adaptive multi bit-plane image steganography using block data-hiding,” Multimedia Tools and Applications, vol. 75 , no. 14 , pp. 8319–8345, Jul. 2016, doi: 10.1007/S11042-015-2752-9/TABLES/8
125 Doğan Ş. , “A new data hiding method based on chaos embedded genetic algorithm for color image,” Artificial Intelligence Review, vol. 46 , no. 1 , pp. 129–143, Jun. 2016, doi: 10.1007/S10462-016-9459-9/TABLES/4
126 Bunker G. , “Targeted cyber-attacks: how to mitigate the increasing risk,” Network Security, vol. 2020 , no. 1 , pp. 17–19, Jan. 2020, doi: 10.1016/S1353-4858(20)30010-6
127 Lallie H. S. , Shepherd L. A. , Nurse J. R. , Erola A. , Epiphaniou G. , Maple C. , et al , “Cyber security in the age of COVID-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic,” Computer Security, vol. 105 , p. 102248, Jun. 2021, doi: 10.1016/j.cose.2021.102248 36540648
128 Kshetri N. , “Cybercrime and Cybersecurity in Africa,” Journal of Global Information Technology Management, vol. 22 , no. 2 , pp. 77–81, Apr. 2019, doi: 10.1080/1097198X.2019.1603527
129 Liu J. , Ke Y. , Zhang Z. , Lei Y. , Li J. , Zhang M. , et al , “Recent Advances of Image Steganography with Generative Adversarial Networks,” IEEE Access, vol. 8 , pp. 60575–60597, 2020, doi: 10.1109/ACCESS.2020.2983175
130 Goodfellow I. , Pouget-Abadie J. , Mirza M. , Xu B. , Warde-Farley D. , Ozair S. , et al . (2014). Generative adversarial nets. Advances in neural information processing systems, vol. 27 , pp. 1–9, 2014.
131 Subramanian N. , Elharrouss O. , Al-Maadeed S. , and Bouridane A. , “Image Steganography: A Review of the Recent Advances,” IEEE Access, vol. 9 , pp. 23409–23423, Mar. 2021, doi: 10.1109/ACCESS.2021.3053998
132 Li Y. , Ling B. , Hu D. , Zheng S. , and Zhang G. , “A Deep Learning Driven Feature Based Steganalysis Approach,” Intelligent Automation and Soft Computing, vol. 37 , no. 2 , Aug. 2023, doi: 10.32604/iasc.2023.029983
133 Kuznetsov A , Luhanko N , Frontoni E , Romeo L , Rosati R ., “Image steganalysis using deep learning models,” Multimedia Tools and Applications, vol. 83 , no. 16 , pp. 48607–48630, May. 2024, doi: 10.1007/s11042-023-17591-0
134 Li H , Wang J , Xiong N , Zhang Y , Vasilakos AV , Luo X ., “A siamese inverted residuals network image steganalysis scheme based on deep learning,” ACM Transactions on Multimedia Computing, Communications and Applications, vol. 19 no. 6 , pp. 1–23, Jul. 2023, doi: 10.1145/35791
135 Martín A. , Hernández A. , Alazab M. , Jung J. , and Camacho D. , “Evolving Generative Adversarial Networks to improve image steganography,” Expert Systems with Applications, vol. 222 , p. 119841, Jul. 2023, doi: 10.1016/j.eswa.2023.119841
136 Wu D. C. and Tsai W. H. “A steganographic method for images by pixel-value differencing,” Pattern Recognition Letters, vol. 24 , no. 9–10 , pp. 1613–1626, Jun. 2003, doi: 10.1016/S0167-8655(02)00402-6
137 Islam M. R. , Siddiqa A. , Uddin M. P. , Mandal A. K. , & Hossain M. D. , “An efficient filtering based approach improving LSB image steganography using status bit along with AES cryptography,” 2014 International Conference on Informatics, Electronics and Vision, ICIEV 2014, 2014, doi: 10.1109/ICIEV.2014.6850714
138 Chen H.J. , “Linking employees’ e-learning system use to their overall job outcomes: An empirical study based on the IS success model,” Computers and Education, vol. 55 , no. 4 , pp. 1628–1639, Dec. 2010, doi: 10.1016/J.COMPEDU.2010.07.005
139 Ioannidou A. , Halkidis S. T. , and Stephanides G. , “A novel technique for image steganography based on a high payload method and edge detection,” Expert Systems with Applications, vol. 39 , no. 14 , pp. 11517–11524, Oct. 2012, doi: 10.1016/J.ESWA.2012.02.106
140 Webb G. I. , Pazzani M. J. , and Billsus D. , “Machine learning for user modeling,” User Model User-adapt Interact, vol. 11 , no. 1–2 , pp. 19–29, 2001, doi: 10.1023/A:1011117102175/METRICS.
141 Jordan M. I. and Mitchell T. M. , “Machine learning: Trends, perspectives, and prospects,” Science (1979), vol. 349 , no. 6245 , pp. 255–260, Jul. 2015, doi: 10.1126/SCIENCE.AAA8415/ASSET/AB2EF18A-576D-464D-B1B6-1301159EE29A/ASSETS/GRAPHIC/349_255_F5.JPEG
142 Majeed A. “Improving Time Complexity and Accuracy of the Machine Learning Algorithms Through Selection of Highly Weighted Top k Features from Complex Datasets,” Annals of Data Science, vol. 6 , no. 4 , pp. 599–621, Dec. 2019, doi: 10.1007/S40745-019-00217-4/TABLES/10
143 Al-Shaarani F , and Gutub A. , “Securing matrix counting-based secret-sharing involving crypto steganography,” Journal of King Saud University-Computer and Information Sciences. Vol 34 , no. 9 , pp. 6909–6924, Oct. 2022, doi: 10.1016/j.jksuci.2021.09.009
144 Hureib E. S. and Gutub A. A. , “Enhancing medical data security via combining elliptic curve cryptography with 1-LSB and 2-LSB image steganography,” International Journal of Computer Science and Network Security (IJCSNS), vol. 20 , no. 12 , pp. 232–241, Dec. 2022.
145 Al-Shaarani F. and Gutub A. , “Securing matrix counting-based secret-sharing involving crypto steganography,” Journal of King Saud University-Computer and Information Sciences, vol. 34 , no. 9 , pp. 6909–6924, Oct. 2022, doi: 10.1016/j.jksuci.2021.09.009
146 Hassan F. S. and Gutub A. , “Efficient reversible data hiding multimedia technique based on smart image interpolation,” Multimedia Tools and Applications, vol. 79 , no. 39 , pp. 30087–30109, Oct. 2020, doi: 10.1007/s11042-020-09513-1
147 Hassan F. S. and Gutub A. , “Novel embedding secrecy within images utilizing an improved interpolation-based reversible data hiding scheme,” Journal of King Saud University-Computer and Information Sciences, vol. 34 , no. 5 , pp. 2017–3030, May. 2022, doi: 10.1016/j.jksuci.2020.07.008
148 Gutub A. and Al-Shaarani F. , “Efficient implementation of multi-image secret hiding based on LSB and DWT steganography comparisons,” Arabian Journal for Science and Engineering, vol. 45 , no. 4 , pp. 2631–2644, Apr. 2020, doi: 10.1007/s13369-020-04413-w
