Stego Video Research Articles

Adaptive multi-cascaded ResNet-based efficient multimedia steganography framework using hybrid mouth brooding fish-emperor penguin optimization mechanism

A massive amount of data is transmitted in the Internet of Things (IoT). Nowadays, the concerning of security issues are the major factor while transferring data through wireless networks. Since, data privacy becomes complicated. In this research work, a newly proposed model for multimedia steganography is developed. Initially, the required video is obtained from the publically available datasets, and then the acquired input is subjected to the Adaptive Discrete Cosine Transformation (DCT) based block process. The optimal blocks are chosen by the Adaptive Multi-cascaded ResNet (AMC-ResNet) model for applying stego data. Here, the parameter optimization takes place in the DCT and ResNet model to enhance the steganography performance via the Mouth Brooding Fish Emperor Penguin Optimization (MBFEPO) derived from the Mouth Brooding Fish Algorithm (MBFA) and Emperor Penguin Optimization Algorithm (EPOA). Finally, the inverse DCT is employed at the blocks to get the final stego video. In the audio steganography phase, the wanted audio is gathered from external websites. The collected data are given to the Short-time Fourier Transform (STFT) to convert into the spectrogram image, and then the spectrogram image is given to the Adaptive DCT block, selecting the block to apply stego data. Thus, the blocks are selected with the utilization of the Adaptive Multi-cascaded ResNet (AMC-ResNet), where the parameters within the DCT and the ResNet are optimized via the same MBFEPO to improve the performance. After, the Inverse ADCT is applied to reconstruct the spectrogram image. Then, the resultant stego audio is obtained by using the Inverse STFT. Finally, several experiments are conducted to estimate the working ability of the proposed steganography model. The outcome of the recommended model shows 12.3%, 52.6%, 12.3%, and 84.3% better performance SFO, HBA, MBFA, and EPOA in terms of median. The recommended model performs superior performance rather than the existing approaches.

AI‐generated video steganography based on semantic segmentation

AbstractTraditional video steganography methods primarily rely on modifying concealed spaces for embedding, thereby exhibiting a certain degree of security and embedding capacity. Nevertheless, these methods do not fully capitalize on the rich semantic information inherent in videos, limiting their overall effectiveness. In this paper, an AI‐generated video steganography scheme based on semantic segmentation is proposed. The mapping relationship between secret and semantic information is established by using a semantic segmentation model. The secret information can be converted into semantic labels by semantic histograms or pixels means, and semantic labels containing secret information are obtained and input into the video‐to‐video model to drive the generation of stego videos. After receiving the stego video, the receiver extracts the secret information using a pre‐defined specific embedding mode, including the methods of sub‐block partitioning and embedding capacity per frame. The experimental results show that the stego video has good visual quality, security, and robustness against various noise attacks.

Video Steganography with DNA and Complex Frames

A modern approach to video steganography combines DNA embedding of encrypted data with intelligent frame selection, enhancing data security during transmission. By creating fake DNA for frame selection and embedding it into strategically chosen frames, termed complex frames this method ensures robust encryption and imperceptibility, offering a promising solution for secure internet data transmission. KeyWords: Complex frames, DNA, embedding, encryption, stego video.

VHNet: A Video Hiding Network with robustness to video coding

Data hiding is an essential practice for ensuring the security of confidential data and secret communication and thus has been intensively studied. The development of deep learning enables the realization of video hiding, which aims to hide a video into another. Different from image data hiding, stego videos generated by video hiding methods need to undergo video coding in real-world scenarios because the unencoded raw videos are too large to be easily transmitted or stored. However, previous video-hiding works did not consider this, leading to a common vulnerability to information loss caused by video coding on stego videos. In other words, previous works considered videos simply as sequences of temporally continuous images, and thus the image sequences output from the data hiding network were fed directly into the recovery network to obtain secret information without video coding. We propose a module called CompressionSimulator to make stego videos robust to video coding. CompressionSimulator is pre-trained to simulate the distortion caused by video coding, and then trained together with the whole framework to make the data hiding network robust to video coding. Furthermore, an invertible neural network based framework is introduced for our video hiding task, i.e. hiding one video in another, in which we design an auxiliary module to capture the spatio-temporal relationships of videos. In general, we develop a video hiding network (VHNet) that provides video coding robustness. We conduct experiments to prove the necessity of incorporating video coding into the design of video hiding networks. Results of experiments conducted on the UCF 101 dataset show that our proposed work outperforms previous methods, and our framework achieves optimal performance in both visual and quantitative comparisons. To the best of our knowledge, we are the first paper to propose a learning-based scheme to improve the robustness of hiding videos with videos.

Building a Statistical Model to Detect Foreground Objects and using it in Video Steganography

Video steganography has become a popular option for protecting secret data from hacking attempts and common attacks on the internet. However, when the whole video frame(s) are used to embed secret data, this may lead to visual distortion. This work is an attempt to hide sensitive secret image inside the moving objects in a video based on separating the object from the background of the frame, selecting and arranging them according to object's size for embedding secret image. The XOR technique is used with reverse bits between the secret image bits and the detected moving object bits for embedding. The proposed method provides more security and imperceptibility as the moving objects are used for embedding, so it is difficult to notice the changes in the moving objects instead of using background area for embedding in the video. The experimental results showed the better visual quality of the stego video with PSNR values exceeding 58 dB, this indicates that the proposed method works without causing much distortion in the original video and transmitted secret message.

High-fidelity video reversible data hiding using joint spatial and temporal prediction

An efficient predictor is essential in reversible data hiding methods. This paper proposes a video reversible data hiding method, where the correlations between pixels in both spatial and temporal domains are fully considered to improve prediction accuracy. In more detail, for each pixel, both intra prediction and inter prediction are implemented and the better prediction mode is selected according to intra or inter local complexity. A double-layered video partition scheme is applied so that the motion vectors generated during inter prediction are not necessarily embedded as additional information while reversibility is guaranteed. In addition, a cover pixel selection principle based on the probability distribution of the prediction errors is proposed, which prevents the pixels with large prediction errors from being modified. Experimental results demonstrate that the joint spatial and temporal prediction scheme offers satisfactory accuracy, and the proposed video reversible data hiding method can obtain high-quality stego videos.

A Modified Enhanced Method of Audio – Video Steganography for High Security Data Transmission

Steganography is an art of convert communication, which offers secrete and secure way of communication. It has many application areas such as audio-video synchronization,in defense forces etc… The audio data is converted into binary format and encrypted using a XOR encryption algorithm. Then the encrypted audio data converted into XL data form. The video frames are divided into non-overlapping blocks, and the difference between the adjacent pixels in each block is computed to obtain a reference matrix. Each block is then iterated through, if the difference between adjacent pixels is greater than or equal to the average difference in the reference matrix, the MPVD (Modified Pixel Value Differencing) based LSB of the pixel value is modified to embed the encrypted audio data bit. The embedded bit is then XORed with the corresponding LSB of adjacent pixel in the same block. The modified video frames can be sent over an unsecured channel without revealing the embedded encrypted audio data. To extract the encrypted audio message from the stego video, the embedded bits are XORed with the corresponding LSB of the adjacent pixel in the same block, and the modified LSBs are extracted and decrypted using the same encryption algorithm and key.

4D chaotic system-based secure data hiding method to improve robustness and embedding capacity of videos

Chaotic systems are used in various applications due to their high security and complexity. Because minor changes in parameters have a significant effect on the output values and the dynamic behavior of the system. Besides, security and robustness are the most important requirements of data hiding. Therefore, in this paper, it is proposed to use chaos-based data hiding for videos. The merits of the proposed method are selection of embedding location with robust system, increasing the secret data capacity, and keep the visual quality, which are in trade-off for existing methods. Because, in the existing methods the secret message is embedded into pixels of a consecutive, a selected or a random frame. Another deficiency of existing methods is embedding capacity. They embed fewer secret data to keep the visual quality of the stego object. However, compared to the drawback of existing literature, in the proposed method the secret message is embedded in entire video with 4D chaotic system, which is also an important novelty of this paper.The proposed method evaluated by objective criteria and achieved valuable results. The histogram analysis proves our method is undetectable. For different high ratios of embedding capacity, the visual quality of the stego videos is high where the average PSNR is about 55 dB, and MS-SSIM is closed to 1. The proposed method's results compared chaotic and non-chaotic video steganography methods. Our methods achieve 54.92 dB for embedding ratio 8.08% where the results are 53.57 dB and 5% for the best existing non-chaotic method and 49.99 dB and 0.001% for the best chaotic method, for similar capacity the proposed method gives superior visual performance over the chaotic literature. Finally, the robustness analysis with pixel value attack and lsb attack shows the proposed method appears to be resistant to these types of attacks.

Robust video steganography framework based on channel-space attention

In robust video steganography, a message is embedded into a video such that video distortions are avoided while producing a stego video of imperceptible difference from the cover video. Traditional techniques achieved robustness against particular distortions but are complicated in computation and design, and rely on different compression standards. Nowadays, deep-learning-based methods can achieve impressive visual quality and robustness to attacks. We propose a framework with a channel-space attention mechanism for robust video steganography. The framework is composed of depthwise separable convolution layers that can learn channel-space segments for embedding and extraction. The secret messages are distributed across channel-space scales to increase imperceptibility and robustness to distortions. This end-to-end solution is trained with the 3-player game approach to conducting robust steganography, where three networks compete. Two of these handle embedding and extraction operations, while the third network simulates attacks and detection from a steganalyst as an adversarial network. Comparative results versus recent research show that our method is more robust against compression and video distortion attacks. Peak signal-to-noise ratio and structural similarity index were used for evaluating visual quality and demonstrate the imperceptibility of our method.

An End-to-End Video Steganography Network Based on a Coding Unit Mask

Steganography hides secret messages inside the covers while ensuring imperceptibility. Different from traditional steganography, deep learning-based steganography has an adaptable and generalized framework without needing expertise regarding the embedding process. However, most steganography algorithms utilize images as covers instead of videos, which are more expressive and more widely spread. To this end, an end-to-end deep learning network for video steganography is proposed in this paper. A multiscale down-sampling feature extraction structure is designed, which consists of three parts including an encoder, a decoder, and a discriminator network. Furthermore, in order to facilitate the learning ability of network, a CU (coding unit) mask built from a VVC (versatile video coding) video is first introduced. In addition, an attention mechanism is used to further promote the visual quality. The experimental results show that the proposed steganography network can achieve a better performance in terms of the perceptual quality of stego videos, decoding the accuracy of hidden messages, and the relatively high embedding capacity compared with the state-of-the-art steganography networks.

A chaotic video steganography technique for carrying different types of secret messages

Increasing the security of data in insecure communication networks is the motivation of researchers to propose different types of methods. Steganography is one of the security techniques that is used in videos, images, sounds, and other digital data. This technique embeds important data or secret messages into cover media to prevent unauthorized access. This paper proposes a new method for video steganography to develop our security purposes. The proposed method uses integer wavelet transform to increase the security and quality of video frames. This paper also presents an improved three-dimensional Sine chaotic map and uses it in the processes of the proposed video steganography. Hiding different types of messages with various sizes is the main aim of the suggested algorithm. The simulation results show that the proposed method has good imperceptibility and performance after the embedding process. Furthermore, the results of extracted messages after applying some noises on the stego videos, and the regular/singular (RS) steganalysis results demonstrate the acceptable robustness against some noises. Also, comparison results show that the proposed method outperforms some similar approaches.

An Animated GIF Steganography Using Variable Block Partition Scheme

The paper presents a novel Graphics Interchange Format (GIF) Steganography system. The algorithm uses an animated (GIF) file format video to apply on, a secured and variable image partition scheme for data embedding. The secret data could be any character text, any image, an audio file, or a video file; that is converted in the form of bits. The proposed method uses a variable partition scheme structure for data embedding in the (GIF) file format video. The algorithm estimates the capacity of the cover (GIF) image frames to embed data bits. Our method built variable partition blocks in an empty frame separately and incorporate it with randomly selected (GIF) frames. This way the (GIF) frame is divided into variable block same as in the empty frame. Then algorithm embeds secret data on appropriate pixel of the (GIF) frame. Each selected partition block for data embedding, can store a different number of data bits based on block size. Intruders could never come to know exact position of the secrete data in this stego frame. All the (GIF) frames are rebuild to make animated stego (GIF) video. The performance of the proposed (GIF) algorithm has experimented and evaluated based on different input parameters, like Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) values. The results are compared with some existing methods and found that our method has promising results.

Comparison of Three Proposal Methods in Steganography Encryption Secret Message using PVD and MapReduce

This paper will present a comparison between three proposed methods. All of these methods include hiding a secret message inside a video for the aim of transferring it to another party with high security and a high embedding rate in order to ensure that the secret message is not discovered by the attacker. In addition , facilitating deals with video frames as large data for the purpose of analyzing, dividing and controlling frames easily by the programmer, using the MapReduce method. This is done by dividing the video into a series of frames, and before the hiding process, the message is encrypted using the advance encryption standard (AES) algorithm. These basic processes are implemented in all three proposed methods, the rest of the details for each method are: The first method: used the pixel value difference (PVD) algorithm to hide the secret message in the video. In addition, the stego secret key was also used. This key is used for the purpose of deciding the locations of the pixels that will be employed to hide the secret message inside it. The second method: the MapReduce principle is used for the purpose of facilitating dealing with video frames. The chosen frame will enter the MapReduce stages. This is implemented by dividing the frame into three matrices red, green, blue (RGB). Each matrix represents a map. Moreover, the technique that is used for concealment is the least significant bit (LSB) technology which uses the stego secret key (x2) for the purpose of selecting sites that will be hidden by it. The third method: Also, the MapReduce principle is used, but this method is implemented by dividing the frame into four blocks. Each block represents a map. In one of the stages of the MapReduce, the hiding process will be done by using the (PVD) method which uses the stego secret key (n+15). Finally, the reducer, which is the last stage, will collect the results of each block to generate the stego-frame. The results of the three methods are efficiency, transparency, robustness and powerful in stego video. It is noticed that the second method has achieved the lowest capacity, thus achieving high security. As for the third method, it achieved the highest capacity and the highest execution time was the first method. Despite this, all the three methods have achieved high security. The attacker or unauthorized person cannot detect any suspicious differences in a stego video. These results are obtained through using many measurements: peak signal-to-noise ratio (PSNR), Mean Squared Error (MSE), Entropy and correlation coefficient. Index Terms—steganography, PVD, LSB, MapReduce, Secret key.

An efficient DCT-SBPM based video steganography in compressed domain

In this research work, an efficient robust video steganography in compressed domain is presented based on discrete cosine transform (DCT) encoded with H.264 codec. In this method, a compressed video (.mp4) of different resolutions, frame rates and sizes are used as a cover and RGB image is used as a secret message. Using appropriate stego key, the secret frames from the sequence of video frames are selected and the non-dynamic pixels of these frames are identified. The least significant bits (LSB) of the DCT component of these non-dynamic pixels are used as carrier object for hiding the R, G, and B components of secret message individually. An appropriate embedding key is designed to embed secret message in cover video such that the quality assessment parameters viz. Peak signal-to-noise ratio (PSNR) and mean square error (MSE) of imperceptibility can be optimized. Also, the successful extraction of secret message from the cover stego video is assessed by the robustness parameters viz. Bit error rate (BER) and similarity (Sim). The hiding capacity of proposed video steganography with respect to secret message and cover frame is measured by using hiding ratio (HR). The efficiency of proposed video steganography method is compared with the reported methodologies on the basis of the parameters of imperceptibility and robustness.

Steganography algorithm based on modified EMD-coded PU partition modes for HEVC videos

As High Efficiency Video Coding (HEVC) is a worldwide popular video coding standard, the steganography of HEVC videos has gained more and more attention. Prediction unit (PU) is one of the most important innovative modules of HEVC; thus, PU partition mode-based steganography is becoming a novel branch of HEVC steganography. However, the embedding capacity of this kind of steganography is limited by the types of PU partition modes. To solve the problem, modified exploiting modification direction (EMD)-coded PU partition mode-based steganography is proposed in this paper, which can hide a secret digit in a (2n + x − 1)-ary notational system in a pair of PU partition modes and thus enlarging the capacity. Furthermore, two mapping patterns for PU partition modes are analyzed, and the one that performs the better is selected as the final mapping pattern. Firstly, 8 × 8- and 16 × 16-sized PU partition modes are recorded according to the optimal mapping pattern in the video encoding process. Then, PU partition modes are modified by using the proposed method to satisfy the requirement of secret information. Finally, the stego video can be obtained by re-encoding the video with the modified PU partition modes. Experimental results show that the embedding capacity can be significantly enlarged, and compared with the state-of-the-art work, the proposed method has much larger capacity while keeping high visual quality.

DDCA: A Distortion Drift-Based Cost Assignment Method for Adaptive Video Steganography in the Transform Domain

Cost assignment plays a key role in coding performance and security of video steganography. Existing cost assignment methods (for adaptive video steganography) are designed for specific transform coefficients rather than all transform coefficients. In addition, existing video steganographic frameworks do not allow Syndrome-Trellis Codes (STCs) to modify all transform coefficients in both intra-coded and inter-coded frames at the same time. To address these limitations, in this article, we first propose a novel video steganographic framework. Then, we give a theoretical analysis of distortion drift in both intra- and inter-coding procedures. Based on the analysis, we design a Distortion Drift-Based Cost Assignment method, hereafter referred to as DDCA. DDCA considers the inner-block, inter-block and inter-frame distortion costs in order to improve the coding performance and the security of stego videos when the embedding payload is fixed. We conducted extensive experiments using two video datasets to evaluate the proposed video steganographic framework and DDCA, in terms of the coding performance and the security. Our experiments show that the proposed framework outperforms three recent state-of-the-art methods, for example the coding performance and the security of stego videos can benefit from DDCA by making full use of all nonzero transform coefficients.

A hybrid DST-SBPNRM approach for compressed video steganography

The proposed research work is presenting a novel approach in the field of steganography, especially in the compressed video domain with optimum imperceptibility to secure the secret information. In this approach, the specific secret cover video frames are selected from the sequence of video frame from which the non-dynamic region is separated. Discrete sine transform (DST) transforms this non-dynamic region from spatial domain to frequency domain. The least significant bits (LSBs) of the integer part of DST components are used to conceal the secret data. The H.264 codec is used to construct the compressed stego video using intra-frame, inter-frame prediction, motion vector estimation, transform coefficient, i.e., Discrete Cosine Transform (DCT), quantization, and entropy coding. The efficiency of the proposed hybrid technique “DST- Secret Bit Positions of Non-dynamic Region for Message (DST-SBPNRM)” for video steganography is measured by evaluating imperceptibility, robustness and embedding capacity. Moreover, the proposed technique is experimented on the well-defined video dataset and the obtained results are compared with the related work to validate the significance of the proposed work.

Blind MV-based video steganalysis based on joint inter-frame and intra-frame statistics

Despite all its irrefutable benefits, the development of steganography methods has sparked ever-increasing concerns over steganography abuse in recent decades. To prevent the inimical usage of steganography, steganalysis approaches have been introduced. Since motion vector manipulation leads to random and indirect changes in the statistics of videos, MV-based video steganography has been the center of attention in recent years. In this paper, we propose a 54-dimentional feature set exploiting spatio-temporal features of motion vectors to blindly detect MV-based stego videos. The idea behind the proposed features originates from two facts. First, there are strong dependencies among neighboring MVs due to utilizing rate-distortion optimization techniques and belonging to the same rigid object or static background. Accordingly, MV manipulation can leave important clues on the differences between each MV and the MVs belonging to the neighboring blocks. Second, a majority of MVs in original videos are locally optimal after decoding concerning the Lagrangian multiplier, notwithstanding the information loss during compression. Motion vector alteration during information embedding can affect these statistics that can be utilized for steganalysis. Experimental results have shown that our features’ performance far exceeds that of state-of-the-art steganalysis methods. This outstanding performance lies in the utilization of complementary spatio-temporal statistics affected by MV manipulation as well as feature dimensionality reduction applied to prevent overfitting. Moreover, unlike other existing MV-based steganalysis methods, our proposed features can be adjusted to various settings of the state-of-the-art video codec standards such as sub-pixel motion estimation and variable-block-size motion estimation.

Robust video data security using hybrid cryptography-steganography technique

The interest in the digital videos confidentiality in the current electronic and interrelated world has increased. Thus, this paper aimed at making a video steganography scheme for an acceptable security with high speed of calculation by embedding data (video frames) in other video frames. The techniques of embedding and encrypting video frames in a cover video file were done through two ways. Firstly, two keys and XOR bit operation were employed to create a large range of different keys for encryption. Secondly, a modified method of Least Significant Bit (LSB) technique was used for hiding high resolution video frames (bitmap color) in selective cover video frames, offering two security layers. The procedures of encrypting and hiding video data were successfully tested on many secret videos such ad Eye Video, Secret Medicine Video and Traffic Video. All experiments were conducted using MATLAB-GUI software, representing an efficient and easy tool for video management supported by powerful testing tools as histograms and mathematics for video quality. Experimental result demonstrated a good performance with low correlation and very high PSNR of the Stego video frames.

Video Steganography using IWT, DWT, LBP Methods and its Comparative Research

Steganography is a method used for inserting the secret information into an another medium such as text, images, audio signals or video signals, without revealing its existence in the medium. In video steganography, a video file will be used as a cover medium and secret message embedded inside the cover medium. Video are picture perfect for embedding the secret message because of its large embedding capacity. We propose three secure steganography algorithms that embed a bit stream of the secret message into the approximation coefficients of the integer wavelet transform(IWT), DWT and using LBP method to form stego video. The geometric alteration between the cover video and the stego video is measured by using the Mean Square Error (MSE) and the PSNR. The new results show that, the proposed algorithms can hide the secret message with a great payload capacity with a high level of security and a greater invisibility.