Video Steganography PDF

Video steganography  Video steganography Video steganography is the process of hiding secret information inside videos. The secret information can be any media like text, audio, images, video, and binary files and the carrier video can be raw/compressed in any...

Video steganography  Video steganography Video steganography is the process of hiding secret information inside videos. The secret information can be any media like text, audio, images, video, and binary files and the carrier video can be raw/compressed in any format. Video steganography methods based on different criteria: The first classification level is based on the format of the cover video. The cover video considered are either in the raw domain or compressed domain videos  Raw domain videos Steganography Raw domain videos are classified into spatial domain and transform domain. Least Significant Bits (LSB) substitution and other significant methods are included in the spatial domain. Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are the extensively used transformation methods to convert the cover videos into the transform domain Raw domain videos Steganography Source 1 Hierarchical classification of video steganography methods  Video steganography in raw domain The raw domain-based video steganography methods consider the cover video as a sequence of frames and the data embedding operation is applied to each frame separately. The general data embedding procedure in the raw domain: Initially, the cover video sequence is transformed into multiple frames. The secret data is hidden inside the frames using various methods. In the raw domain, the secret data is directly embedded in the spatial domain of the cover frame, or the cover frame is transformed into the frequency domain and secret data is embedded in the frequency domain. Video steganography in raw domain Video Steganography – Data hiding in the spatial domain The Least Significant Bits-based methods are the commonly used algorithms for image, audio, and video steganography. LSB methods are simple and effective. Usually, LSB methods are described as k-LSB substitution methods where k stands for the number of secret bits that can be hidden. Based on the embedding algorithm, the value of k is changed. The hiding capacity of the embedding algorithm depends on the number of bits (k) that can be manipulated in the cover video  Video Steganography – Data hiding in the spatial domain Most of the LSB-based approaches included the cover pixel selection technique, secret data encoding as well encryption techniques to improve the security and robustness of the data- hiding algorithm. “HASH LSB” , an extended version of the LSB approach integrated hash function along with the LSB substitution method. The proposed LSB scheme followed a ‘3-3-2’ embedding pattern to hide the secret data in the cover image. For a cover pixel, the ‘3-3-2 LSB pattern’ utilizes 3 LSBs of the red component, 3 LSBs of the green component, and 2 LSBs of the blue component for hiding the data. Encryption and computer forensics have been employed with the 4 LSB method for data embedding. Although encryption techniques can provide additional security for data, the proposed method is more prone to attack. The 4 LSB substitution can cause significant visual degradation to the cover video after embedding the secret data  Video Steganography – Data hiding in the spatial domain Adaptive steganography approaches for the LSB methods: The adaptive steganography approach hides the secret data in a specific predefined region of interest ( moving objects, skin regions, etc. ) Edges of the objects in the cover frame are chosen for predominantly hiding the secret data The Canny edge detection technique is utilized for detecting the edges. The 4-LSB method is used for embedding the secret data in the detected edge pixels of the cover frame. Non-edge pixels are also utilized for hiding the data by using the 2-LSB method. The RSA algorithm is employed to provide additional security Non-LSB methods in the spatial domain of raw videos K-means clustering and LBP features to embed the secret data. The cover frames are converted into Lab color space and the K-means clustering algorithm is implemented to group the cover frames into different clusters. Only selected clusters of the cover frames are chosen for embedding the secret data. LBP methodology is utilized for hiding the secret data in the selected cluster of the cover frame. Average histogram values of the cover frames to determine the suitable frames from cover video sequences for embedding the secret data.  Non-LSB methods in the spatial domain of raw videos The data hiding scheme to embed the secret data in random RGB components of the cover frame. The pixels on the cover frame are randomly permuted using a random key (seed) and a pseudo-random number generator. Every 8 bits of the secret message are embedded in the random pixel by following the specific order “RGBBGRGG”. It means the first and fifth bit of the secret message is embedded in the red component of the pixel, the third and fourth bit in the blue component of the pixel, and the rest of the bits are embedded in the green component of the pixel.  Data hiding in transform domain DCT Discrete wavelet transform Wavelet transform can be either continuous or discrete. Discrete Wavelet transformation is of interest in image processing tasks as it is simple, operational, and effective. Discrete Wavelet Transform (DWT) decomposes the signal into sets with significant and insignificant information. The significant information is related to general appearance and is called low- frequency DWT coefficients. Similarly, the insignificant information represents the behavior of the signals and is called the high-frequency coefficients. A single signal is passed through a set of filters and decomposed into two parts - approximation and details. wavelet transform  Discrete wavelet transform The rows and columns of an r × c image are passed and processed independently. The formula used for decomposing the rows and columns are given in (1) and (2) respectively. The low pass components are arranged in the top half while the high pass components are arranged in the bottom half. The same steps are repeated for several iterations based on the application. Every time the transformation is applied to the low-frequency components. The data hiding technique based on the LSB substitution approach in the wavelet domain  Discrete wavelet transform the DWT operation is performed to decompose the video into frequency subbands. The secret data is embedded in the HH subbands using the LSB approach Approaches for hiding the secret video inside a cover video in the wavelet domain. DWT operation is applied to the cover frames and the LSB substitution approach is used to hide the secret frames in the HL, HH, and LH subbands of the cover frame  Discrete Cosine Transform DCT Discrete cosine transform DCT is also a transform function like DWT that divides the image into spectral subbands. The major difference between DCT and DWT is the earlier one generates more frequency bands and provides higher frequency resolution. DWT generates few frequency bands and provides high spatial resolution. A significant amount of works in the literature used the DWT domain to embed the secret data in raw videos. DCT domain is not frequently used to hide the secret data inside the raw videos. Video steganography methods proposed in the compressed domain have utilized the DCT domain extensively for hiding secret data.  Discrete Cosine Transform DCT In the raw video domain, the two-dimensional DCT is applied to each frame of the video separately and transforms the frame into low, middle, and high-frequency bands. The secret data is hidden in the transform coefficients of either one or multiple bands. Consider an arbitrary frame I of resolution J × K. And T is the transformed frame generated by applying DCT on I. The DCT coefficients of T are calculated using the equation, After embedding the bits of secret data in the DCT coefficients, inverse two dimensional DCT is applied on the frame T to generate frame I using the equation, two- dimensional  Discrete Cosine Transform DCT After embedding the bits of secret data in the DCT coefficients, inverse two dimensional DCT is applied on the frame T to generate the frame I using the equation, Here, Ijk represents the pixel value in the cell jk (column j and row K) of the frame I. Further, Txy represents the transform coefficient corresponds to the cell xy (column x and row y) of the 2D-DCT matrix  Video Steganography in Compressed Domain The majority of video steganography methods proposed in the literature for data hiding in the raw domain are simple and easy to implement. But, they are more prone to various attacks, especially compression attacks. Furthermore, currently, videos in compressed form are preferred for storing as well as transmitting over the internet. The compressed video requires less storage space compared to the uncompressed video. Transferring the videos in compressed form over the internet is quicker and requires less bandwidth. In this context, the data hiding techniques in the compressed video domain have gained popularity in the last two decades. On the other hand, compression causes the removal of redundant video data and reduces the space for hiding more data.  Video steganography in compressed domain Among various available video compression coding standards, MPEG-X and H.26X are the widely utilized methods in recent years. Specifically, H.264/AVC a.k.a MPEG-4 Part-10 video coding standard is the popular and predominant video codec used  Video Steganography in Compressed Domain The H.264 video codec has multiple novel features compared to its predecessors and some of the novel features are “multiple frames reference capability”, “flexible macroblock ordering”, Intra prediction in intraframe, etc. Generally, H.264 video codec consists of multiple groups of pictures (GOP). And each GOP contains the intra-coded frames (I-frame), predicted frames (P-frame), and bidirectional predicted frames (B-frame). The I-frame a.k.a keyframe is the one that is independently coded and the first frame of each GOP. The P-frame contains only the difference between the current frame and the preceding frame. The B- frame holds only the changes in the current frame from both the previous and following frames  Video Steganography in Compressed Domain Cont. Encoding 1. The initial frame (which contains all the important data and is considered the I-frame ) is divided into macroblocks where each macroblock consists of 16 × 16 pixels. 2. The data compression process comprises various steps such as prediction, domain transformation, and encoding. 3. The prediction utilizes the temporal and spatial redundancy in the video data. 4. Prediction allows encoding the difference between the previously coded data and the predicted data. There are two types of prediction: Intra-prediction and inter-prediction. Intra prediction generates the prediction of macroblocks based on previously coded data in the current frame while inter prediction generates the prediction based on the data in the previously coded frames.  Video Steganography in Compressed Domain 5- Motion estimation and motion compensation techniques are utilized to predict the frame. The difference between the prediction and the current macroblock is known as residual. 6- The block of residuals is subjected to domain transformation using integer transform. DCT is the most commonly used integer transform. 7- The block of transformed coefficients is quantized to minimize the precision of the coefficients. 8- The final step of encoding converts the various values ( quantized DCT coefficients, data required by the decoder to reconstruct the prediction, other data about the video sequence, etc. ) obtained in the previous steps and syntax elements to binary codes. In the compressed domain, the data hiding is implemented in two ways; data hiding along with the video encoding procedure and data hiding in the encoded bit stream. An Overview Of Data Hiding In Compressed Domain Lab/Assignment 4 Python Extracting the audio using “ffmpeg” from the video. We can use FFmpeg to combine all the frames with a hidden message to form a video and then lay out the audio.  References Kunhoth, Jayakanth, et al. "Video steganography: recent advances and challenges." Multimedia Tools and Applications (2023): 1-43 Aly HA (2010) Data hiding in motion vectors of compressed video based on their associated prediction error. IEEE Trans Inf Forensics Security Bhawna KS, Singh V et al (2021) Information hiding techniques for cryptography and steganography. I n: Singh V, Asari VK, Kumar S (eds) Computational methods and data engineering. Springer Singapore, Singapore, pp 511–527