Robust video steganography method and device based on adaptive quantization

Abstract

The present invention relates to a robust video steganography method and device based on adaptive quantization. The secret message embedding process of the method includes: extracting PCA features from the steganographic video; constructing a mapping relationship between PCA features and QIM quantization step lengths; and using an adaptive QIM algorithm to perform adaptive video steganography embedding based on the mapping relationship between PCA features and QIM quantization step lengths. The secret message blind extraction process of the method includes: extracting PCA features from the steganographic video; querying the corresponding quantization step length based on the PCA feature value; and using an adaptive QIM algorithm to extract the secret message based on the queried quantization step length. The present invention applies the adaptive QIM algorithm to video for the first time, adaptively determines the quantization step length based on the heavily compressed channel and video content, and minimizes embedding distortion and improves security under the premise of the same robustness. At the same time, the scheme combines error correction code technology to achieve reliable covert communication on social network channels.

Description

Technical Field

[0001] The present invention belongs to the field of information security and is a robust video steganography method and device based on adaptive quantization, which can resist video recompression and is used for covert communication in lossy channels. Background Art

[0002] Information hiding technology in the field of information security consists of two parts: steganography and steganalysis. The former mainly studies how to embed secret information into carriers such as images, audio, and video to achieve the purpose of covert communication, while the latter uses machine learning, pattern recognition and other methods to distinguish between ordinary carriers and steganographic files.

[0003] Generally speaking, when designing a steganography algorithm, the following factors need to be considered comprehensively:

[0004] 1) Imperceptibility: The carrier before and after steganography must be indistinguishable to the human senses. This means that it is impossible to determine whether the carrier has been steganographically altered solely through human hearing and vision.

[0005] 2) Robustness: The embedded secret information must be reliably and completely recovered after the stego file undergoes signal processing operations or malicious modification;

[0006] 3) Embedding capacity: When covert communication is required, the amount of secret information data embedded in the carrier file should be increased as much as possible while ensuring a certain level of steganographic security;

[0007] 4) Embedding efficiency: Under the premise of embedding a certain amount of information, the degree of modification to the carrier should be minimized;

[0008] 5) Security: The steganographic embedding operation is required to not cause significant disturbance to the statistical characteristics of the carrier file, so that the steganalyst (attacker) cannot detect the existence of steganographic behavior using simple statistical features.

[0009] Thanks to the development of social networks, multimedia sharing has become increasingly popular. This has provided new application scenarios for covert communication. Covert communication based on multimedia sharing can conceal the relationship between communicating parties and can transmit secret messages one-to-many. However, most social networking sites and multimedia sharing platforms recompress uploaded multimedia files, such as videos and images, to reduce transmission bandwidth and storage costs. This poses a significant challenge to covert communication based on multimedia sharing. Researchers need to develop robust digital media steganography methods that can resist recompression attacks by social networking platforms in order to achieve covert communication based on multimedia sharing.

[0010] After years of research by experts and scholars, robust image steganography has made significant progress and development, resulting in the emergence of numerous robust steganography algorithms resistant to JPEG recompression. However, with the rapid advancement of internet technology and the continuous evolution of video coding standards, video has become the most popular and irreplaceable communication medium. Its advantage over images lies in its richer application scenarios and ability to transmit greater amounts of information. Therefore, video is gradually replacing images as a more suitable medium for steganography. However, due to the complexity of video recompression, research on robust video steganography is still in its early stages and can be broadly categorized into two types based on the domain in which the secret message is embedded:

[0011] 1) Pixel-domain-based video steganography: The main idea is to embed secret messages by modifying the pixel values ​​or pixel value distribution of video frames. For example, some scholars have proposed combining hash indexing with the least significant bit (LSB) technique to embed secret messages by modifying the pixel values ​​of video frames.

[0012] 2) Video steganography algorithms based on spatial transform domains: The main idea is to transform the pixel values ​​of video frames into another domain using matrix transformations, and then modify them in the transform domain to embed the secret message. For example, some scholars have proposed using discrete wavelet transforms (DWTs) to transform the pixel values ​​of video frames into the frequency domain, and then embed the secret message by modifying the low-frequency subbands.

[0013] As another area of ​​robust steganography research, researchers have proposed numerous robust modulation schemes, such as spread spectrum (SS) modulation, quantization index modulation (QIM), and coefficient-relationship-based modulation. Considering robustness and blind extraction of secret information, many robust steganography methods utilize QIM to embed secret messages. The QIM algorithm maps carrier elements to quantization indices based on a preset fixed quantization step size, then modulates the quantization index to embed the message bits. Quantization indices are typically set empirically, ignoring the inherent characteristics of the carrier elements. Therefore, QIM algorithms cannot guarantee an optimal balance between robustness and quantization distortion. Subsequently, in the field of image steganography, researchers proposed an adaptive QIM algorithm based on the Watson visual model. This algorithm adaptively allocates quantization steps based on the carrier content, achieving optimal visual effects while maintaining consistent robustness. However, both JPEG compression and stego-embedding alter the image content, making it difficult for the recipient to accurately determine the preset quantization step size, leading to potential errors in information extraction.

[0014] To reduce the bit error rate (BER), researchers have also introduced error correction codes to process the embedded secret information and decode the extracted secret information to recover the erroneous bits. Commonly used error correction codes include BCH codes and RS codes. Summary of the Invention

[0015] The purpose of this invention is to provide a new robust video steganography scheme that is resistant to video recompression. This scheme, for the first time, applies the adaptive QIM algorithm to video. The quantization step size is adaptively determined based on the recompression channel and video content. While maintaining robustness, this scheme minimizes embedding distortion and improves security. Furthermore, this scheme, combined with error-correcting code technology, enables reliable covert communication over social network channels.

[0016] Compared with other robust video steganography algorithms, the present invention applies the adaptive QIM algorithm for the first time, which can adaptively determine the appropriate quantization step size according to the video content and compression channel noise, and improve the security of the steganography algorithm while ensuring the robustness of the algorithm. At the same time, the present invention extracts a robust principal component analysis (PCA) feature and establishes a mapping between PCA features and quantization step sizes. At the embedding end, the quantization step size is allocated according to the eigenvalue to complete the adaptive steganography embedding. At the extraction end, the PCA feature is extracted, and the corresponding quantization step size is queried according to the eigenvalue to achieve correct secret information extraction. Since the PCA feature itself is robust to video recompression, the receiver can accurately obtain the preset quantization step size at the extraction end to avoid incorrect secret information extraction.

[0017] Specifically, the technical solutions adopted by the present invention are as follows:

[0018] A robust video steganography method based on adaptive quantization includes a secret message embedding process based on adaptive QIM, wherein the secret message embedding process includes the following steps:

[0019] Extract PCA features from the carrier video;

[0020] Construct the mapping relationship between PCA features and QIM quantization step size;

[0021] According to the mapping relationship between PCA features and QIM quantization step size, the adaptive QIM algorithm is used for adaptive video steganographic embedding.

[0022] Furthermore, extracting PCA features from the video includes:

[0023] 1) Video preprocessing: If the input video is in MP4 format, use FFMPEG to decode the video into YUV components. If the input video is in RAW format YUV, proceed directly to the next step without processing.

[0024] 2) Extract block PCA features: Divide the Y component into non-overlapping 32×32 blocks; perform dual-tree complex wavelet transform (DTCWT) on each block to obtain the second-level low-frequency subband LL. Use PCA to obtain the proportion of the first principal component of LL, which is used as the PCA feature of the block; divide the PCA eigenvalues ​​into N subintervals, and divide blocks with the same PCA eigenvalues ​​into the same subinterval.

[0025] Furthermore, constructing a mapping relationship between PCA features and quantization step sizes includes:

[0026] 1) Determine the mapping relationship between PCA feature subintervals and quantization step sizes: The PCA feature effectively reflects the content characteristics of the 32×32 block itself. In order to adaptively determine the quantization step size based on the block content, the quantization step size to be determined is also set to N, achieving a one-to-one mapping between the PCA feature subintervals and the quantization step size. First, set the maximum and minimum values ​​of the quantization step size corresponding to each interval, and take the average as the initial value for steganalysis embedding. Then, after channel compression, calculate the error rate of each subinterval, and use binary search to update the maximum and minimum values ​​of the quantization step size of each subinterval. Finally, repeat the above operation until the difference between the maximum and minimum values ​​of the quantization step size assigned to each subinterval is less than 1. Take the average of the maximum and minimum values ​​of the quantization step size of each subinterval as the final quantization step size of each subinterval.

[0027] 2) Set the quantization step size of the 16×16 block of the U component: Step 1) constructs a mapping relationship between the subinterval of the PCA eigenvalue of the 32×32 block and the quantization step size, and the blocks in the same subinterval are assigned the same quantization step size; in the YUV420 format, the 32×32 block in the Y component corresponds one-to-one with the 16×16 block in the U component; therefore, the quantization step size of each 16×16 block of the U component is equal to the quantization step size assigned to the 32×32 block of the Y component at the corresponding position.

[0028] Furthermore, the adaptive video steganographic embedding is performed using the adaptive QIM algorithm according to the mapping relationship between the PCA feature and the quantization step size, including the following steps:

[0029] 1) Extract the carrier element to be embedded: Obtain the U component and divide it into non-overlapping 16×16 blocks. Perform a DWT transform on each block to obtain the low-frequency subband LL. Perform a singular value decomposition (SVD) on the LL to obtain a diagonal singular value matrix. Extract the maximum singular value as the carrier element of the block. Repeat the above steps until all blocks are extracted.

[0030] 2) BCH encoding of the secret message: read the bit stream to be embedded from the file to be embedded, encode the read 0-1 bit stream using BCH code to generate an encoded message sequence; set a scrambling key k, scramble the message sequence, and generate the data sequence to be embedded;

[0031] 3) Using the adaptive QIM algorithm to modulate the carrier element sequence and generate the stego sequence: Based on the mapping relationship between PCA features and quantization step sizes, the quantization step size of the carrier element corresponding to each 16×16 block is obtained. The carrier element is modulated using the adaptive QIM algorithm and embedded into the scrambled data to generate the stego sequence.

[0032] 4) Reconstruct the 16×16 blocks to generate the encrypted U component sequence: replace the original carrier elements with the steganographic elements, write the singular value diagonal matrix, perform the inverse SVD transform, and generate a new low-frequency subband LL. Perform the inverse DWT transform to reconstruct the new LL subband and the LH, HL, and HH subbands into the encrypted 16×16 blocks. Repeat the above steps until all 16×16 blocks are reconstructed and the blocks are spliced ​​in order to generate the encrypted U component sequence.

[0033] 5) Encode the YUV sequence to generate a steganographic video: Use FFMPEG to jointly encode the encrypted U component sequence and the original Y and V component sequences and encapsulate them into an MP4 steganographic video.

[0034] A robust video steganography method based on adaptive quantization includes a blind extraction process of a secret message based on adaptive QIM, wherein the blind extraction process of the secret message includes the following steps:

[0035] Extract PCA features from steganographic videos;

[0036] Query the corresponding quantization step size according to the PCA eigenvalue;

[0037] According to the quantization step size of the query, an adaptive QIM algorithm is used to extract the secret message.

[0038] Furthermore, the PCA feature extraction from the steganographic video includes:

[0039] 1) Video preprocessing: If the input video is in MP4 format, use FFMPEG to decode the video into YUV components. If the input video is in RAW format YUV, proceed directly to the next step without processing.

[0040] 2) Extract block PCA features: Divide the Y component into non-overlapping 32×32 blocks, perform DTCWT on each block, obtain the second-level low-frequency subband LL, use PCA to obtain the first principal component ratio of LL, and use it as the PCA feature of the block.

[0041] Furthermore, querying the corresponding quantization step size according to the PCA eigenvalue includes:

[0042] Querying the quantization step size corresponding to the 16×16 block of the U component: In the YUV420p format, the 32×32 blocks in the Y component correspond one-to-one with the 16×16 blocks in the U component. The quantization step size at the same position is set to the same value on the embedding side. Based on the extracted PCA eigenvalues ​​of the 32×32 block, the subinterval in which it falls is queried. Using the preset mapping between PCA eigenvalue intervals and quantization step sizes, the quantization step size assigned on the embedding side is obtained and used as the quantization step size for the 16×16 block of the U component at that position.

[0043] Furthermore, extracting the secret message using an adaptive QIM algorithm according to the quantization step size of the query includes:

[0044] 1) Extract the steganographic element sequence: Divide the U component into non-overlapping 16×16 blocks. Perform a DWT transform on each block to obtain the low-frequency subband LL. Perform SVD decomposition on the LL to obtain a diagonal singular value matrix. Extract the maximum singular value as the steganographic element for that block. Repeat this process until all blocks are extracted.

[0045] 2) Using the adaptive QIM algorithm to extract the message sequence carried by the stego-element sequence: Based on the 16×16 block quantization step of the query U component, the adaptive QIM algorithm is used to extract the message data of the stego-element sequence and generate the message sequence;

[0046] 3) BCH decoding of the message sequence to generate the embedded secret message: Use the scrambling key k to descramble the extracted message sequence, and then perform BCH decoding on the descrambled message sequence to generate the secret message.

[0047] A robust video steganography device based on adaptive quantization is characterized by comprising a secret message embedding unit and / or a secret message extraction unit; the secret message embedding unit adopts the method of the present invention to embed a secret message to generate a stego video; the secret message extraction unit adopts the method of the present invention to extract the secret message from the stego video.

[0048] The robust video steganography method of the present invention has the following beneficial effects on the relevant technical fields:

[0049] 1) Stronger robustness. Currently, all compressed domain-based video steganography methods are not robust. The main reason is that video transcoding will greatly change the video syntax elements, such as transform coefficients, quantization parameters, motion vectors, etc., which destroys the steganographic embedding domain. The present invention is a video steganography algorithm based on the spatial domain. Changes to video syntax elements will not affect the spatial domain features. Therefore, the present invention has strong robustness and can resist video recompression and transcoding. Compared with other spatial domain video steganography methods, this method takes video content and channel noise into consideration in its design. By setting an adaptive quantization step size, the bit error rate is reduced, and it has stronger robustness.

[0050] 2) Embedded disturbance can be adaptively controlled, thus improving security. The embedded disturbance of currently available video steganography algorithms is only related to the parameters of the algorithm itself, and has nothing to do with the content of the video carrier and the channel noise. During transmission over a lossy channel, different videos will be introduced with varying degrees of noise, and the noise in different areas of the same video will also be different. The algorithm parameters set by manual experience cannot guarantee the minimum embedded disturbance, thus affecting the security of the steganography algorithm. The present invention can adaptively determine the quantization step size of each block according to the content of the video carrier and the channel noise, thereby minimizing the overall embedded disturbance and improving the security of the algorithm.

[0051] 3) It can be used in social network channels, enabling reliable blind extraction of secret messages. Because the social network transcoding mechanism is a closed black box, social network channels are inherently uncertain. Existing robust video steganography algorithms are unable to achieve reliable covert communication via social networks. These algorithms suffer from high bit error rates in social network channels or require the original video to assist in extracting secret messages. The adaptive quantization process of the present invention is highly robust, enabling reliable covert communication on social networks. Furthermore, the secret message extraction process does not require the original video or auxiliary information, ensuring blind extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 Schematic diagram of the dual-tree complex wavelet transform of the present invention;

[0053] Figure 2 1. It is a schematic diagram of the quantization step size binary iterative search process of the present invention;

[0054] Figure 3 is a secret message embedding flow chart of the present invention;

[0055] Figure 4 Schematic diagram of discrete wavelet transform of the present invention;

[0056] Figure 5 It is a flow chart of secret message extraction of the present invention. DETAILED DESCRIPTION

[0057] The present invention will be described below in conjunction with the embodiments and drawings.

[0058] In this embodiment, a robust video steganography method based on adaptive quantization is provided, wherein the process of constructing a mapping relationship between PCA features and quantization step sizes includes the following steps:

[0059] 1) Video preprocessing: If the input video is in MP4 format, use FFMPEG to decode the video into YUV components. If the input video is in RAW format YUV video, proceed directly to the next step without processing.

[0060] 2) Calculate the PCA features of the Y component block: Get the video Y component sequence and divide it into 32×32 pixel blocks. For a given 32×32 block X i , perform a 2-level DTCWT transformation (such as Figure 1 As shown), generate low frequency sub-band right Perform PCA and calculate The covariance matrix of The singular value matrix ∑=diag(σ1,σ2,…,σ r ,…,0). Calculate X i PCA eigenvalues ​​of Perform the above operation for all 32×32 blocks to obtain the PCA eigenvalues ​​of all blocks of the Y component. Divide the PCA eigenvalues ​​into N subintervals (N = 12 in the implementation process), that is, all PCA subintervals m = {m1, m2, ..., m 12}, where m1 = (0, 0.5], m2 = (0.5, 0.9], m i =(0.87+0.01×i,0.88+0.01×i], i=3,4,…,12. If the PCA eigenvalues ​​of the blocks are in the same subinterval, the quantization step sizes assigned to them are the same.

[0061] 3) Determine the mapping relationship between PCA feature subintervals and quantization step sizes: In step 2), 12 PCA feature subintervals are divided, and the blocks in the same subinterval will be assigned the same quantization step size. In order to minimize the overall embedding disturbance while ensuring robustness, the present invention needs to set a quantization step size for each subinterval m. i Search for an optimal quantization step size q i , so that the error rate of each subinterval is less than the threshold T, and the quantization step size q i Minimum. In the iterative search q i In the process (such as Figure 2 ), do the following:

[0062] 3.1) is the PCA feature subinterval m iSet the initial quantization step: Set the quantization step search range for all PCA sub - intervals m, and set the maximum quantization step Set the minimum quantization step Set the initial quantization step to Where

[0063] 3.2) Extract the embedding - to - be coefficients of 16×16 blocks of the U - component of the video, and perform downward quantization: Extract the embedding - to - be coefficients c=(c1, c2, …, c n ) of all 16×16 blocks of the U - component. According to the initial quantization step Δ=(q1, q2, …, q n ), perform downward quantization on the embedding - to - be coefficients to generate the quantized coefficients That is

[0064] 3.3) Reconstruct the U - component sequence and perform video compression: Replace the original embedding - to - be coefficients c with the quantized coefficients s to generate a new U - component sequence, and compress the YUV components

[0065] 3.4) Extract the compressed coefficients and calculate the error rate of each interval: Extract the compressed quantized coefficients of all 16×16 blocks of the U - component If there exists a block X i , such that Then this block may have an error. Count the number of error - prone blocks in each sub - interval and calculate the error rate e=(e1, e2, …, e 12 )

[0066] 3.5) Set the threshold T and update the quantization step search range: Set the error rate threshold T. If e i <T, then update the maximum value of the quantization step of the i - th sub - interval Otherwise, update the minimum value of the quantization step of the i - th sub - interval Use the updated quantization step range to calculate the new quantization step

[0067] 3.6) Repeat steps 3.2) to 3.5) until q max -q min ≤1. Through binary iterative search, the final quantization step is

[0068] 4) Set the quantization step size for the 16×16 U component blocks: Steps 2) and 3) construct a mapping between the subintervals of the PCA eigenvalues ​​for the 32×32 blocks and the quantization step size. Blocks in the same subinterval are assigned the same quantization step size. In the YUV420p format, the 32×32 blocks in the Y component correspond one-to-one with the 16×16 blocks in the U component. Therefore, the quantization step size for each 16×16 U component block is equal to the quantization step size assigned to the corresponding 32×32 Y component block.

[0069] Furthermore, based on the mapping relationship between PCA features and quantization steps (m→q s ), a robust video steganography method based on adaptive quantization, in which the embedding process of secret information is as follows Figure 3 As shown, the following steps are included:

[0070] 1) Extract the carrier element to be embedded: Get the U component and divide it into non-overlapping 16×16 blocks. Assume that each block X i , for X i Perform DWT transformation (such as Figure 4 As shown), generate four sub-bands HH, HL, LH, and LL, and obtain the LL low-frequency sub-band Perform SVD decomposition on it Obtain the singular value diagonal matrix ∑=diag(σ1,σ2,…,σ r ,…,0), extract the maximum singular value σ1 as the block X i The above operation is repeated until all blocks are extracted. The final extracted U component carrier element sequence c=(c1,c2,…,c n ).

[0071] 2) BCH encoding of the secret message: Read the embedded bit stream m from the file to be embedded, use BCH (15,5) to encode the read 0-1 bit stream, and generate the encoded message sequence. Set a scrambling key k, scramble the message sequence, and generate the embedded data sequence m k .

[0072] 3) Use the adaptive QIM algorithm to modulate the carrier element sequence to generate the stego sequence: According to the mapping relationship between PCA features and quantization step size (m→q s ), obtain the U component carrier element sequence c=(c1,c2,…,c n ) corresponds to the quantization step size Δ=(q1,q2,…,q n ), using the adaptive QIM algorithm to modulate the carrier element, given the i-th carrier element c i and the data bit m to be embedded k (i), then the generated i-th steganalytic element After the message is embedded, the steganographic element sequence s=(s1,s2,…,s n ).

[0073] 4) Reconstruct the 16×16 blocks and generate the encrypted U component sequence: Given the i-th steganographic element s i , replacing the original carrier element c i , generate the modulated singular value diagonal matrix ∑=diag(s i ,σ2,…,σ r ,…,0), perform SVD transformation to generate a new LL subband Perform inverse DWT transformation to combine HH, HL, LH and LL subbands into new LL blocks Repeat the above steps until all the encrypted blocks are generated. All 16×16 blocks are concatenated in sequence to generate a sequence of encrypted U components.

[0074] 5) Encode the YUV sequence to generate a steganographic video: Use FFMPEG to jointly encode the encrypted U component sequence and the original YV component sequence and encapsulate them into an MP4 steganographic video.

[0075] The mapping relationship between PCA subintervals and quantization steps (m→q s ) and the scrambling parameter k will be passed to the recipient as the key parameters in the covert communication process.

[0076] Furthermore, the above method also includes a process of extracting secret information, such as Figure 5 As shown, the following steps are included:

[0077] 1) Video preprocessing: If the input video is in MP4 format, use FFMPEG to decode the video into YUV components. If the input video is in RAW format YUV video, proceed directly to the next step without processing.

[0078] 2) Calculate the PCA features of the Y component block: Get the video Y component sequence and divide it into 32×32 pixel blocks. For a given 32×32 block X i , perform a 2-level DTCWT transformation to generate a low-frequency subband right Perform PCA and calculate The covariance matrix of The singular value matrix ∑=diag(σ1,σ2,…,σ r ,…,0). Calculate X i PCA eigenvalues ​​of Perform the above operation for all 32×32 blocks to obtain the PCA eigenvalues ​​p=(p1, p2, …, p n ).

[0079] 3) Query the quantization step size corresponding to the 16×16 block of the U component: Assume that the PCA eigenvalue of the i-th 32×32 block of the Y component is p i , query p i The subinterval j where p i ∈m j ,i=1,2,…,n;j=1,2,…,12. According to the mapping relationship between PCA interval and quantization step, query subinterval m j The corresponding quantization step size q j Repeat the above operation until the corresponding quantization step size is found for all 32×32 blocks in the Y component. In the YUV420p format, the 32×32 blocks in the Y component correspond to the 16×16 blocks in the U component one by one. The quantization step size at the same position is set to the same value at the embedding end. Therefore, the quantization step size corresponding to all 16×16 blocks in the U component is

[0080] 4) Extract the steganographic element sequence: Get the U component and divide it into non-overlapping 16×16 blocks. Assume that each block X i , for X i Perform DWT transformation to generate four sub-bands: HH, HL, LH, and LL, and obtain the LL low-frequency sub-band Perform SVD decomposition on it Obtain the singular value diagonal matrix ∑=diag(σ1,σ2,…,σ r ,…,0), extract the maximum singular value σ1 as the block X i The above operation is repeated until all blocks are extracted. The final extracted steganalytic element sequence of the U component is s=(s1,s2,…,s n ).

[0081] 5) Using the adaptive QIM algorithm, extract the message sequence carried by the steganographic element sequence: According to the quantization step size Δ generated in step 4) = (q1, q2, ..., q n ) and the steganalytic element sequence s=(s1,s2,…,s n ), execute the message extraction process of the adaptive QIM algorithm, Generate message sequence m k =(m1,m2,…,m n ).

[0082] 6) BCH decodes the message sequence to generate the embedded secret message: Use the scrambling key k to descramble the extracted message sequence m k , and then perform BCH decoding on the descrambled message sequence to generate the secret message m.

[0083] From the above specific implementations, it can be seen that the present invention constructs a mapping relationship between PCA feature subintervals and quantization step sizes (m→q s ), can adaptively set a reasonable quantization step size based on the carrier video pixel block content and compression channel noise Under the premise of ensuring robustness, the embedding disturbance is reduced as much as possible and the security is improved. In the embedding process of the present invention, the embedding process of the adaptive QIM algorithm is executed. The embedding of the secret message is completed when the steganographic element s i The distortion disturbance is less than q i / 2, the secret message can still be accurately extracted, ensuring the robustness of the present invention against video recompression. In the secret message extraction process of the present invention, the PCA eigenvalue of the 32×32 block of the Y component can be used to restore the quantization step size set by the embedding end. This approach eliminates the need for additional auxiliary information, ensuring blind message extraction. Furthermore, the present invention incorporates error correction coding technology, using BCH codes to enhance error correction capabilities and reduce the bit error rate. This approach utilizes a rational design approach encompassing adaptive quantization step size, steganographic modulation algorithm, and error correction code, ensuring robustness against video recompression and enhancing security by reducing embedded perturbations.

[0084] Based on the same inventive concept, another embodiment of the present invention provides a robust video steganography device based on an audio side channel, comprising a secret message embedding unit and / or a secret message extraction unit; the secret message embedding unit adopts the secret message embedding process in the above-mentioned method to embed a secret message to generate a stego video; the secret message extraction unit adopts the secret message extraction process in the above-mentioned method to extract the secret message from the stego video.

[0085] Based on the same inventive concept, another embodiment of the present invention provides an electronic device (computer, server, smart phone, etc.), which includes a memory and a processor, the memory stores a computer program, the computer program is configured to be executed by the processor, and the computer program includes instructions for executing each step in the method of the present invention.

[0086] Based on the same inventive concept, another embodiment of the present invention provides a computer-readable storage medium (such as ROM / RAM, disk, CD), which stores a computer program. When the computer program is executed by a computer, it implements the various steps of the method of the present invention.

[0087] The above examples are only used to illustrate the technical solutions of the present invention rather than to limit the same. Those skilled in the art may modify or make equivalent substitutions for the technical solutions of the present invention without departing from the spirit and scope of the present invention. The scope of protection of the present invention shall be based on the claims.

Claims

1. A robust video steganography method based on adaptive quantization, characterized in that: The method includes a secret message embedding process based on an adaptive QIM, wherein the secret message embedding process includes the following steps: Extract PCA features from the carrier video; Construct the mapping relationship between PCA features and QIM quantization step size; According to the mapping relationship between PCA features and QIM quantization step size, the adaptive QIM algorithm is used for adaptive video steganographic embedding; The constructing of the mapping relationship between PCA features and quantization step sizes includes: The number of quantization steps to be determined is set to N, achieving a one-to-one mapping between PCA feature subintervals and quantization steps. First, the maximum and minimum quantization steps corresponding to each interval are set, and the mean is taken as the initial value for steganalysis embedding. Then, after channel compression, the error rate of each subinterval is calculated, and the maximum and minimum quantization steps of each subinterval are updated using binary search. Finally, the above operation is repeated until the difference between the maximum and minimum quantization steps assigned to each subinterval is less than 1. The mean of the maximum and minimum quantization steps of each subinterval is taken as the final quantization step of each subinterval. Then, the quantization step size of the U component 16×16 block is set. The quantization step size of each U component 16×16 block is equal to the quantization step size allocated to the Y component 32×32 block at the corresponding position.

2. The method according to claim 1, characterized in that The extracting PCA features from the carrier video includes: If the input video is in MP4 format, FFMPEG is used to decode the video into YUV components. If the input video is in RAW format, it will proceed to the next step without processing. The Y component is divided into non-overlapping 32×32 blocks. A dual-tree complex wavelet transform is performed on each block to obtain the second-level low-frequency subband LL. PCA is used to obtain the proportion of the first principal component of LL, which is used as the PCA feature of the block. The PCA eigenvalues ​​are divided into N subintervals, and blocks with the same PCA eigenvalues ​​are divided into the same subinterval.

3. The method according to claim 1, characterized in that The method of using the adaptive QIM algorithm to perform adaptive video steganography embedding according to the mapping relationship between PCA features and quantization step lengths includes the following steps: Obtain the U component and divide it into non-overlapping 16×16 blocks. Perform DWT transformation on each block to obtain the low-frequency subband LL. Perform singular value decomposition on LL to obtain the singular value diagonal matrix. Extract the maximum singular value as the carrier element of the block. Repeat the above operation until all blocks are extracted. Read the bit stream to be embedded from the file to be embedded, encode the read 0-1 bit stream using BCH code to generate an encoded message sequence; set a scrambling key k, scramble the message sequence, and generate a data sequence to be embedded; According to the mapping relationship between PCA features and quantization step length, the quantization step length of the carrier element corresponding to each 16×16 block is obtained. The carrier element is modulated using the adaptive QIM algorithm and the scrambled data is embedded to generate a steganographic element sequence. Replace the original carrier elements with the stego elements, write the singular value diagonal matrix, perform an inverse SVD transform, and generate a new low-frequency subband LL. Perform an inverse DWT transform and reconstruct the new LL subband and the LH, HL, and HH subbands into a 16×16 encrypted block. Repeat the above steps until all 16×16 blocks are reconstructed, and then concatenate the blocks in sequence to generate the encrypted U component sequence. Using FFMPEG, the encrypted U component sequence and the original Y and V component sequences are jointly encoded and encapsulated into an MP4 steganographic video.

4. A robust video steganography method based on adaptive quantization, characterized in that: The invention comprises a blind extraction process of a secret message based on an adaptive QIM, wherein the blind extraction process of the secret message extracts a secret message from a steganographic video generated by the method according to any one of claims 1 to 3, and the blind extraction process of the secret message comprises the following steps: Extract PCA features from steganographic videos; Query the corresponding quantization step size according to the PCA eigenvalue; According to the quantization step size of the query, an adaptive QIM algorithm is used to extract the secret message.

5. The method according to claim 4, characterized in that The PCA feature extraction for the steganographic video includes: If the input video is in MP4 format, FFMPEG is used to decode the video into YUV components. If the input video is in RAW format, it will proceed to the next step without processing. Divide the Y component into non-overlapping 32×32 blocks, perform DTCWT on each block, obtain the second-level low-frequency subband LL, use PCA to obtain the first principal component ratio of LL, and use it as the PCA feature of the block; The querying of the corresponding quantization step size according to the PCA eigenvalue includes: In the YUV420p format, the 32×32 blocks in the Y component correspond one-to-one to the 16×16 blocks in the U component, and the quantization step sizes at the same position are set to the same value at the embedding end. Based on the PCA eigenvalues ​​of the extracted 32×32 blocks, the subinterval in which they are located is queried, and then the quantization step size allocated at the embedding end is obtained using the preset mapping relationship between the PCA feature interval and the quantization step size, and this is used as the quantization step size for the 16×16 block of the U component at that position.

6. The method according to claim 4, characterized in that The method of extracting the secret message by using an adaptive QIM algorithm according to the quantization step size of the query includes: Divide the U component into non-overlapping 16×16 blocks, perform DWT transformation on each block, obtain the low-frequency subband LL, perform SVD decomposition on the LL, obtain the singular value diagonal matrix, extract the maximum singular value as the steganographic element of the block, and repeat the above operation until all blocks are extracted; According to the 16×16 block quantization step of the query U component, the adaptive QIM algorithm is used to extract the message data of the steganalysis element sequence and generate the message sequence; The extracted message sequence is descrambled using the scrambling key k, and then the descrambled message sequence is decoded by BCH to generate the secret message.

7. A robust video steganography device based on adaptive quantization, characterized in that: It includes a secret message embedding unit and / or a secret message extraction unit; the secret message embedding unit adopts the method described in any one of claims 1 to 3 to embed the secret message to generate a steganographic video; the secret message extraction unit adopts the method described in any one of claims 4 to 6 to extract the secret message from the steganographic video.

8. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, the computer program is configured to be executed by the processor, and the computer program comprises instructions for executing the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a computer, the method according to any one of claims 1 to 6 is implemented.

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