
==== Front
MethodsX
MethodsX
MethodsX
2215-0161
Elsevier

S2215-0161(24)00339-X
10.1016/j.mex.2024.102887
102887
Engineering
A Mellin transform based video steganography with improved resistance to deep learning steganalysis for next generation networks
R B Sushma sushmarb@jnnce.ac.in
a
G R Manjula grmanjula@jnnce.ac.in
a
Belavagi Manjula C. manjula.cb@manipal.edu
b⁎
a JNNCE Shivamogga, Visvesvaraya Technological University, Belagavi, Karnataka 590018, India
b Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
⁎ Corresponding author. manjula.cb@manipal.edu
14 8 2024
12 2024
14 8 2024
13 10288713 6 2024
31 7 2024
© 2024 The Authors. Published by Elsevier B.V.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
In the era of 5 G network advancements, the potential for extremely robust, less-latency, and huge-capacity communication opens up new perspective for multimedia. Steganography enables embedding of sensitive data within multimedia files, making it unreadable to unauthorized third parties. Notably, when using videos as cover, the capacity for data embedding is substantially increased. Recent developments in steganography have largely revolved around modified versions of transform domain techniques. Due to this repetitiveness, it becomes easier for steganalytic tools in detecting concealed data. Addressing this issue, our paper introduces an innovative data embedding approach MARVIS based on the Mellin transform. The superiority of the proposed approach is exhibited using the metrics, MSE, PSNR, and SSIM. MARVIS has achieved PSNR of 50–60 dB and SSIM of 0.9998 for embedding 4 bits of secret data, outperforming other methods that achieve 40 dB for 1 bit. By quadrupling stego capacity, we can embed more secret data per pixel without compromising the integrity of the cover object.• MARVIS utilizes phase modulation for data embedding, offering advantages beyond traditional frequency domain techniques which use frequency domain for data embedding.

• The effectiveness of the proposed data embedding approach is validated through Y-Net, a deep learning-based steganalysis tool.

Graphical abstract

Image, graphical abstract

Keywords

Transform domain
Y-Net
Data hiding
Information security
Method name

MARVIS-Mellin trAnsform based Robust VIdeo Steganography]
==== Body
pmcSpecifications tableSubject area:	Computer Science	
More specific subject area:	Steganography	
Name of your method:	MARVIS-Mellin trAnsform based Robust VIdeo Steganography]	
Name and reference of original method:	NIL	
Resource availability:	NIL	

Background

Real-time data transmission is currently an established and economical means of communication globally. This rise in multimedia data usage has increased multimedia sharing as a fundamental component of internet services [1]. Additionally, the emergence of fifth-generation networks (5 G) has set up fresh possibilities for multimedia distribution services. Compared to 4 G, 5 G and higher technology provides substantially more flexibility, higher bandwidth, enhanced data integrity, increased capacity, and incredibly low latency [[2], [3], [4]]. Steganography has become a popular technique for secretly embedding data into multimedia files over time. This technique exploits the inherent redundancies in human visual perception and statistical characteristics present in multimedia content [[1], [5], [6]]. Cover medium can be any one among text, image audio or video. Opting for video as the cover medium has the distinct advantage of virtually limitless space for embedding concealed information. The various elements of the steganography model are depicted in Fig. 1.Fig. 1 Steganographic system.

Fig 1

A typical steganography process as shown in Fig. 1 involves two primary entities: the secret data to be concealed, and second, the cover medium in which this secret data is embedded. Initially, a video is chosen as the cover medium. Video, in this context, is seen as a sequence of frames, from which a particular frame is chosen based on a pre-decided algorithm. The selected frame and the secret data is given as input to embedding module to get the stego frame. This frame is then merged with the cover video to get stego video. On the receiver side, the stego frames are input to extraction algorithm to get the secret data.

Data can be embedded either in spatial domain or frequency domain. The goal of spatial domain embedding is to directly embed the data into the cover medium in order to modify the spatial properties of the data. Significant methodologies within the spatial domain include LSB (Least Significant Bit) and its derivative techniques. Contrarily, frequency domain embedding involves concealing data inside the cover medium's frequency components. Initially, the cover medium temporal features are extracted and the secret data is embedded. Discrete Cosine Transform (DCT), DWT (Discrete Wavelet Transform), FFT (Fast Fourier Transform), and IWT (Inverse Wavelet Transform) are frequently used techniques in the frequency domain.

These approaches and their variants have been the subject of much study aimed at improving the practice of data embedding. Despite these transform domain techniques being successful, these techniques frequently leave traces that steganalysis can identify. To overcome this a new embedding method MARVIS which performs Mellin Transform for data embedding is introduced. This method performs embedding of secret data in the phase component of the signal, in contrast to DCT and DWT where the real part of frequency component is modified.

Contribution of paper

Video steganography has previously relied on well-established methods such as LSB in spatial domain or transform-domain methods like FFT [[7], [8]], DCT [9], DWT [[5], [10]], IWT [11], and their variants. Despite being successful, these techniques frequently leave traces that steganalysis can identify and also embedding capacity is very less. To address these issues a new embedding method MARVIS which performs Mellin Transform for data embedding is introduced. This method performs embedding of secret data in the phase component of the signal, in contrast to DCT and DWT where the real part of frequency component is modified. The subsequent sections highlight that how Mellin transform can be implemented using FFT, and how its results are superior to other transform domain methods.

Method details

An analytical method for signal processing that goes beyond the Fourier Transform to examine the frequency content and scaling characteristics of signals is the Mellin Transform. This transform, which was first presented by the French mathematician Ernst Mellin, offers insights into how functions behave under scaling operations and is therefore a useful tool [8] in signal processing, image analysis, and pattern identification, among other areas. Fundamentally, the Mellin Transform considers a function's power-law connections in order to capture its scaling behavior. Unlike Fourier Transform, which highlights frequency components, the Mellin Transform looks at a function's behavior as its scale varies. This makes it advantageous when analyzing signals or images that have scaling properties in their features.

The Discrete Mellin transform

The Fast Fourier Transform (FFT) is one of the transform domain steganographic method. It's a powerful algorithm that efficiently transforms a signal from the time domain to the frequency domain. In the context of video frames, the signal represents the pixel intensities. By applying FFT, the image is decomposed into its constituent frequencies, revealing how much variation occurs at high frequencies (sharp edges, details) versus low frequencies (smooth areas). By focusing on the higher-frequency bands of the FFT spectrum, we can identify areas with richer frequency content that can accommodate embedding secret data with less visual distortion in the original video frame.

Discretization plays a crucial role in facilitating the signal to the process of actual data concealing. Let's consider, how Mellin Transform can be discretized and how it can be derived from FFT. The complete detailed derivation of Mellin Transform is given in [12]

Consider a function X(u) which is defined on positive axis ranging from (0,∞), by performing scaling of variableu by a positive number, generates a new functionX′(u) which is related to the original function by the change of variable:(1) u→bu

The transformations within such set collectively constitute a group, and this group is isomorphic to the multiplicative group of positive real numbers. In practical terms, the scaled function is commonly described by the following transformation:(2) X(u)→b1/2X(bu)

Which preserves the value of the standard scalar product(3) (X1,X2)≡∫0∞X1(u)X2*(u)du

in which complex conjugation is indicated by the sign ∗. But there are strong physical grounds for considering broader form of transformations:(4) Db:X(u)→(DbX)(u)≡bq+1X(bu)

Where q is a given real number.

Hence, for any value of q, the set of Db operations form a group. Thus these operations preserves the scalar product:(5) (X1,X2)≡∫0∞X1(u)X2*(u)u2q+1du

Which means:(6) (DbX1,DbX2)≡(X1,X2)

Eq. (5) defines a norm for the functions X(u) on positive on positive axis IR+

This leads to:(7) ∥X2∥≡∫0∞|X(u)|2u2q+1du

The corresponding Hilbert space will be denoted by H2(IR+),u2q+1du. It is treated like any other regular H2 space, with u2q+1du used instead of du in all formulae. Eq. (6) means that the set of operations Db is a unitary representation of the multiplicative group of positive numbers in this space. The particular applications that need to be handled will determine the value of q. It can be useful to utilize a Hilbert space basis in which the operators Db have a diagonal expression when dealing with expressions involving functions affected by dilations of the kind Eq. (4). This causes functions X to be broken down into smaller components, on which the scaling operation can be reduced to a simple multiplication by a complex integer. Such a procedure is familiar when considering the operation which translates a function g(t), t ∈ IR according to:(8) g(t)→g(t−t0)

In that case, the exponentials of the form eλt, are translated by multiplying the functions by a number eλt0 . if λ=iα, where α is a real number, the functions thus generated are unitary representations of the group H2(IR) which provides an orthonormal basis in generalized form in the space provided. The coefficients in the expansion of the function g(t) based on this set are computed through scalar products with the basis elements, constituting the Fourier transform. In this context, similar advancements will establish a connection between the Mellin transformation and the unitary representations of the dilation group in H2(IR+),u2q+1du.

For the sake of simplicity and future reference, the diagonalization of Db will be performed on its infinitesimal form, defined by the operator B. The action of operator B on function X(u) can be expressed as:(9) (BX)(u)≡−12πiddb[(DbX)(u)]b=1

Which simplifies to:(10) B=−12πi(uddu+q+1)

The operator B is self-adjoint, and the unitary representation Db is obtained through exponentiation applied to B as:(11) Db=e2πibB

Where the formal definition of the operator's exponential is expressed through the infinite series:(12) e2πibB=∑n=0∞(−1)n(2πibB)nn!

In this context, the focus is solely on identifying the eigenfunctions, meaning we seek solutions to the following differential equation:(13) BX(u)=γX(u)

Where γ is real

Definition 5: Let X(u) be a function in H2(IR+,u2q+1du). Its Mellin transform is defined by:(14) MT[X](γ)=∫0∞X(u)u2πiγ+rdu

Belongs to L2(IR).

The Inversion formula is given by:(15) X(u)=∫−∞∞MT[X]γu−2πiγ−q−1dγ

It is evident from (14), (15) that Mellin Transform implementation can be efficiently carried out using a Fast Fourier Transform (FFT) algorithm.

Pseudocode

Frame selection Algorithm

FOR each frame_index in cover_video:

Step 1: Apply 2D FFT to the frame:

   fft_frame = np.fft.fft2(frame[:, :, 0])

Step 2: Calculate average energy in high-frequency bands:

   # Assuming fft_frame is a 2D array representing frequency data

   # Select the upper right quarter of the array (higher frequencies)

   high_freq_band = fft_frame[int(fft_frame.shape[0] / 4):, int(fft_frame.shape[1] / 4):]

   high_freq_energy = np.mean(np.abs(high_freq_band))

Step 3: IF `high_freq_energy` is greater than threshold:

   Select the frame for embedding.

Step 4: ELSE: Continue to the next frame (skip embedding).

Embedding Algorithm

Input

cover_video: A 3D array representing the cover video (frames x height x width).

secret_data: A byte array containing the secret data to be embedded. It can be text or grey scale or colored image.

selected_frame: frame selected for embedding.

Output

stego_video: A 3D array representing the stego video with embedded secret data.

Step 1. Initialize stego_video as a copy of cover_video.

Step 2. Initialize secret_index to 0.

Step 3. FOR each selected_frame in cover_video:

   3.1 Apply 2D FFT to the frame

         fft_frame = np.fft.fft2(frame[:, :, 0])

   3.2 Apply FFT shift to center frequencies:

         fft_frame = np.fft.fftshift(fft_frame)

   3.3 Perform logarithmic transformation (to avoid division by zero):

         log_fft_frame = np.log(np.abs(fft_frame) + 1)

   3.4 Separate real and imaginary components:

         real_part, = np.real(log_fft_frame),

         imag_part = np.imag(log_fft_frame)

   3.5 Extract phase information using arctangent:

         phase_angles = np.arctan2(imag_part, real_part)

   3.6 FOR each element i in the phase_angles:

         IF secret_index is less than the length of secret_data:

            Extract current secret data bit(MSB):

            secret_bit = (secret_data[secret_index] >> 7) & 1

            Modify MSB of phase angle phase_angles.flat[i]:

            phase_angles.flat[i] = modify_MSB(phase_angles.flat[i], secret_bit)

            Increment secret_index.

         ELSE: Break the loop (embedding complete).

   3.7 Reconstruct complex components using modified phase:

         modified_real = np.cos(phase_angles)

         modified_imag = np.sin(phase_angles)

         modified_complex = modified_real + 1j * modified_imag

   3.8 Perform inverse logarithmic transform:

          anti_log_frame = np.exp(modified_complex)

   3.9 Apply inverse FFT shift.

   3.10 Apply inverse 2D FFT to recover modified frame.

   3.11 Update stego frame in stego video:

   stego_video[frame_index, :, :, 0] = np.fft.ifft2(anti_log_frame)[:, :, 0].real.astype(np.uint8)

4. Return ‘stego_video’.

Extraction Algorithm

Input

Stego_video: A 3D NumPy array representing the stego video with embedded secret data.

Output

extracted_secret_data: A byte array containing the extracted secret data.

Step 1: Initialize an empty byte array extracted_secret_data.

Step 2: FOR each selected_frame in stego_video:

   2.1 Apply 2D FFT to the grayscale channel (assuming grayscale video):

         fft_frame = np.fft.fft2(frame[:, :, 0])

   2.2 Apply FFT shift to center frequencies:

         fft_frame = np.fft.fftshift(fft_frame)

   2.3 Perform logarithmic transformation (avoid division by zero):

         log_fft_frame = np.log(np.abs(fft_frame) + 1)

   2.4 Separate real and imaginary components:

         real_part, imag_part = np.real(log_fft_frame), np.imag(log_fft_frame)

   2.5 Extract phase information using arctangent:

         phase_angles = np.arctan2(imag_part, real_part)

   2.6 FOR each element i in the flattened phase_angles:

            Extract LSB of the phase angle phase_angles.flat[i]:

            extracted_bit = phase_angles.flat[i]% (2 * np.pi) # Get LSB

            extracted_secret_data.append(extracted_bit << 7) # Append to MSB position

            (initialize with 0)

   IF the length of extracted_secret_data reaches the expected secret data size: Break the loop (extraction complete).

3. Convert extracted bits to form bytes.

4. Return extracted_secret_data.

Method validation

The effectiveness of the proposed method is established by evaluating various parameters. The program is run on a computing system equipped with the following hardware specifications: 12th Gen Intel(R) Core(TM) i9–12,900 K processor running at 3.20 GHz (2.42 GHz with Turbo Boost), 64GB RAM, a 64-bit operating system, and an NVIDIA graphics processor (RTX A2000 12GB).

The results are computed across seven cover videos obtained from the freely downloadable Pexels.com website. All videos share a common resolution of 2160×3840 pixels, maintaining an aspect ratio of 9:16. The videos exhibit varying durations, with the maximum reaching 30 s. Additionally, the frame rate varies across videos, with a maximum of 59 frames per second (FPS). Six distinct secret images are employed for the steganographic process, contributing to a comprehensive evaluation of the proposed method's performance across different video scenarios.

The parameters used for evaluation of the superiority of our method is PSNR, MSE, SSIM, Bits per pixel(BPP) and Embedding capacity.

Fig. 2 provides examples of the cover videos used. The embedding process may utilize multiple frames from each video depending on the amount of secret data and the secret images used for embedding is listed in Fig. 3.Fig. 2 Sample frames of cover videos used.

Fig 2:

Fig. 3 Set of secret images used for embedding.

Fig 3:

Table 1 displays MSE, PSNR and SSIM values for frames listed in Fig. 2 and for secret images detailed in Fig. 3 across different embedding capacities, namely 1-bit, 2-bit, and 4-bit per pixel. Despite the doubling or quadrupling of the number of bits, all the parameter values exhibit minimal variation.Table 1 MSE, PSNR and SSIM values for different embedding capacities.

Table 1:Cover Frame	Secret Images	1 bit	2 bit	4 bit	
MSE x 10−4	PSNR	SSIM 10−3	MSE x 10−4	PSNR	SSIM x10−3	MSE x 10−4	PSNR	SSIM x10−3	
Cover 1	Airplane	72	58.25	998	86	53.14	997	94	50.34	998	
Baboon	54	51.58	999	63	50.32	996	82	47.97	998	
Barbara	89	53.92	997	91	52.13	998	99	49.33	996	
Peppers	41	53.70	999	49	51.98	998	56	49.73	997	
Boat	95	50.79	997	10	48.65	998	44	47.81	998	
Cover 2	Airplane	53	56.30	999	59	55.54	996	69	51.65	997	
Baboon	38	55.48	999	42	55.98	998	63	51.63	999	
Barbara	67	55.62	998	73	55.54	999	81	52.32	998	
Peppers	92	51.15	997	99	51.68	997	109	47.80	997	
Boat	81	53.05	998	88	51.04	999	99	47.61	997	
Cover 3	Airplane	68	56.18	998	74	55.65	997	91	51.45	998	
Baboon	92	59.39	997	95	57.80	998	119	54.19	996	
Barbara	47	50.82	999	52	48.57	997	75	44.79	998	
Peppers	49	50.93	999	58	49.39	999	68	45.75	997	
Boat	76	59.19	998	88	57.54	998	99	54.87	998	
Cover 4	Airplane	98	58.72	997	112	56.13	997	136	53.62	999	
Baboon	57	53.41	998	71	52.99	999	93	50.97	998	
Barbara	44	53.06	999	53	52.76	998	72	48.17	995	
Peppers	87	54.60	997	99	52.99	999	110	48.88	997	
Boat	51	50.50	999	65	48.38	998	80	45.95	996	
Cover 5	Airplane	90	58.11	998	107	57.12	999	121	55.24	999	
Baboon	43	51.42	997	53	50.14	997	72	45.68	996	
Barbara	60	58.55	999	78	56.95	999	92	54.43	998	
Peppers	96	50.23	998	114	48.93	998	124	44.67	997	
Boat	71	56.73	997	81	55.17	997	96	50.67	997	
Cover 6	Airplane	42	58.72	999	48	57.03	998	60	52.22	997	
Baboon	94	58.57	997	99	56.93	996	112	54.64	994	
Barbara	61	55.21	998	67	54.81	997	79	50.38	996	
Peppers	78	55.56	999	87	54.88	998	99	50.43	997	
Boat	57	51.32	997	64	50.83	997	79	44.56	996	
Cover 7	Airplane	49	55.22	998	55	53.94	997	75	50.10	996	
Baboon	81	55.00	998	89	54.45	997	104	49.73	996	
Barbara	75	52.40	999	79	50.51	998	93	45.21	997	
Peppers	60	57.18	998	68	56.81	998	79	54.67	996	
Boat	47	52.54	999	55	51.81	998	72	47.78	997	
Cover 8	Airplane	59	58.85	999	66	56.84	998	78	51.25	997	
Baboon	74	56.19	998	81	55.71	997	96	51.56	996	
Barbara	52	57.70	999	60	56.34	998	79	53.64	997	
Peppers	91	50.95	997	99	49.12	997	114	45.45	997	
Boat	48	57.94	998	57	55.91	998	70	51.67	996	

In the quest for robust steganography, maximizing embedding capacity while maintaining imperceptibility is paramount. MARVIS methodology is tested against diverse embedding capacities, using 9 cover videos and 6 secret images. Even at high embedding rates, MARVIS consistently outperformed existing techniques. It can be easily identified through Figs. 4 and 5. This exceptional performance, despite pushing the boundaries of capacity, highlights the robustness and efficiency of our approach. It demonstrates the potential of MARVIS to revolutionize secure data transmission in scenarios demanding both high capacity and low detectability.Fig. 4 PSNR value comparison for Cover 1, Cover 2, Cover 3 for 200,000, 400,000 and 600,000 bits.

Fig 4:

Fig. 5 MSE value comparison for Cover 1, Cover 2, Cover 3 for 200,000, 400,000 and 600,000 bits.

Fig 5:

Table 2 gives the comparison of the proposed method with other state of the art methods. All these values tabulated here are taken for embedding capacity of 1 bit per pixel. As it can be seen from the Fig. 6, MARVIS majorly outperforms all other methodologies.Table 2 Gives comparative analysis of Mellin transform with other methodologies.

Table 2Secret Images		MDPVDMF [2]	QMDCT [5]	GPVO [13]	AMBTC [14]	Lempel Ziv Welch [15]	Proposed method	
Airplane	MSE	–	–	–	–	1.78	0.0091	
PSNR	–	–	41.08	35.92	46.87	51.4554	
SSIM	–	–	0.9993	–	–	0.9968	
Baboon	MSE	–	–	–	–	2.07	0.0119	
PSNR	38.12	54.32	41.31	29.65	44.98	54.1948	
SSIM	0.98	0.9997	0.9991	–	–	0.9957	
Barbara	MSE	–	–	–	–	1.94	0.0075	
PSNR	38.54	–	40.83	32.26	46.87	44.7935	
SSIM	0.98	–	0.9994	–	–	0.9976	
Peppers	MSE	–	–	–	–	1.87	0.0068	
PSNR	39.94	58.67	40.92	33.66	45.31	45.7588	
SSIM	0.99	0.9996	0.9997	–	–	0.9977	
Boat	MSE	–	–	–	–	1.39	0.0099	
PSNR	39.56	57.29	40.94	31.61	46.7	54.8719	
SSIM	0.99	0.9996	0.9993	–	–	0.9959	
Lena	MSE	–	–	–	–	1.19	0.0084	
PSNR	–	–	40.83	33.97	47.37	46.4731	
SSIM	–	–	0.9996	–	–	0.9961	

Fig. 6 Comparison of MARVIS with current state of the art methods.

Fig 6:

Steganalysis using Y-Net

Deep learning provides a major advancement over conventional techniques in terms of steganalysis [1]. In order to discover secret data, traditional steganalysis uses hand-crafted features, which are time-consuming and can overlook some of the details brought about by more recent steganography approaches. Steganalysis algorithms based on deep learning, gets beyond these restrictions. These models have the ability to learn on their own complex characteristics directly from data, adapting to new steganographic techniques. This flexibility makes it possible to evaluate a steganographic algorithm's capacity to conceal data while maintaining imperceptibility.

In contrary to all-purpose steganalysis models, the architecture of Y-Net is specially designed for the transform domain. With this focused approach, Y-Net is able to concentrate on the particular patterns and artifacts that appear when data is steganographically embedded using transform domain. With this focus, Y-Net is more sensitive to the changes brought about by MARVIS steganographic algorithm, resulting in a more complete and accurate assessment of imperceptibility of the suggested embedding technique.

Y-Net is a deep learning model designed for steganalysis, particularly effective in detecting steganographic messages hidden in images. It utilizes multiple Frequency Domain Convolutional Neural Networks (F-CNNs), operating directly on the frequency representation of the image (obtained through methods like DCT or FFT). Each F-CNN branch analyses a different frequency band, capturing information from various frequency components where steganographic messages might be hidden. Its direct analysis of frequency bands makes it well-suited for detecting steganographic modifications that often target these components. Compared to other models like SR-Net models analyzing only the spatial domain, Y-Net can achieve higher accuracy in steganalysis tasks.

Y-Net resembles a “Y” shape, with two separate branches for processing input data and a single branch for fusion and output. Each branch utilizes Frequency Domain Convolutional Neural Networks (F-CNNs) to extract features from different frequency bands of the image.

Traditional steganography methods like DCT [9], DWT [5], IWT [11], and FFT [7] are easily detected by Y-Net. As shown in Table 3, Y-Net successfully identifies these stego images based on their probability scores exceeding 0.5. However, this effectiveness crumbles against images embedded with the proposed Mellin Transform steganography. Y-Net consistently fails to recognize the stego images generated from MARVIS, evidenced by probability scores consistently below 0.2.Table 3 Y-net steganalysis output.

Table 3:Cover frames	Stego frames	DCT[9]	DWT[5]	IWT[11]	FFT[7]	Proposed method	
Image, table 5	Image, table 5	0.94	0.92	0.93	0.91	0.14	
Image, table 5	Image, table 5	0.92	0.94	0.97	0.93	0.18	
Image, table 5	Image, table 5	0.93	0.94	0.94	0.96	0.11	
Image, table 5	Image, table 5	0.91	0.95	0.92	0.94	0.15	
Image, table 5	Image, table 5	0.92	0.95	0.95	0.91	0.16	
Image, table 5	Image, table 5	0.92	0.91	0.92	0.94	0.21	
Image, table 5	Image, table 5	0.94	0.93	0.96	0.91	0.19	
Image, table 5	Image, table 5	0.95	0.92	0.91	0.96	0.22	
Image, table 5	Image, table 5	0.91	0.94	0.93	0.92	0.24	

Overall comparison

The proposed MARVIS methodology outperforms the existing transform domain techniques in all metrics:• MARVIS offers a different perspective on image characteristics representation compared to traditional techniques. This potentially allows for more effective data hiding strategies by exploiting previously unconsidered aspects of the image data.

• The evaluation using metrics like MSE (Mean Squared Error), PSNR (Peak Signal-to-Noise Ratio), and SSIM (Structural Similarity Index Measure) indicates that MARVIS outperforms existing methods. This translates to a better balance between imperceptibility and embedding capacity.

• Notably, the achieved PSNR remains within the range of 50–60 dB even after doubling or quadrupling the embedding capacity.

Conclusion

MARVIS presents a pioneering approach to data embedding in multimedia files, particularly focusing on videos, in the context of the next generation networks. Due to their repetitive nature, traditional data embedding techniques in the transform domain, such FFT, DCT, DWT, and IWT have demonstrated problems in their vulnerability to steganalysis tools. To handle this issue, a novel approach MARVIS that uses the Mellin transform that offers a unique representation of image characteristics compared to conventional techniques is proposed. Through various experimentation and evaluation utilizing Y-Net and various performance metrics including MSE, PSNR, and SSIM, the method demonstrates the robustness and superiority of the approach. Notably, the achieved PSNR is of 50–60 dB, even after doubling the embedding capacity highlights the strength of the proposed methodology.

Limitations

Not applicable.

Ethics statements

The BOSS Dataset used to train Y-net for steganalysis which is freely downloadable. The secret images used are available on the internet in downloadable format. The cover videos downloaded from pexels.com website. Neither human subjects nor animal models were involved in this research. Additionally, social media data was not used.

Supplementary material and/or additional information [OPTIONAL]

During the preparation of this work the authors used Gemini.ai in order to improve the language and readability. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

CRediT authorship contribution statement

Sushma R B: Conceptualization, Methodology, Software, Writing – original draft. Manjula G R: Conceptualization, Formal analysis, Writing – review & editing. Manjula C. Belavagi: Data curation, Writing – review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

Acknowledgments

This work is funded by Vision Group of Technology(VGST), Government of Karnataka, India under CISEE scheme. Authors would like to express gratitude to Management and Staff of JNN College of Engineering and VGST for their invaluable support.
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