19 results about "Steganographic algorithm" patented technology

The invention discloses a language steganography load enhancement method and device based on a large language model. The method comprises the steps that firstly, a shared semantic restorer obtained through fine adjustment of the large language model LLM is constructed; the sender performs low-redundancy compression and key information protection on the secret message to be processed through the shared semantic restorer; converting the obtained compressed message into a pseudo-random bit stream through index compression coding based on probability; embedding the pseudo-random bit stream into a carrier text generated by a large language model LLM through an underlying steganography algorithm to obtain a steganography text, and transmitting the steganography text through a common channel; and after receiving the steganography text, the receiver losslessly recovers the original secret message through reverse operation based on the steganography algorithm shared with the sender, the coding mode and the shared semantic restorer. According to the method and device, the processing flow of the secret message is optimized through the large language model, and efficient compression and lossless recovery of the secret message are achieved.

The application discloses an AAC security steganography algorithm and system based on a masking effect. The human ear hearing masking effect can cause some audio signals with low intensity to be masked by signals with high intensity, and there is space for hiding secret information. Therefore, the application analyzes a quantization process of MDCT coefficients in AAC coding, records the masked audio signals as modifiable positions, and combines an STC adaptive steganography framework to realize embedding of secret information. Experimental results show that the algorithm can achieve a maximum embedding capacity of 13.61 kbps, can guarantee auditory concealment of speech, and has good security.

The application belongs to the technical field of video encryption transmission, and particularly relates to a video transmission method and system for multimedia video, which comprises the following steps: identity authentication of a receiving end and a sending end; a gray image and an information sequence are randomly generated by the to-be-authenticated end, the information sequence is a binary sequence, and the number of digital 0 and 1 is equal; according to the MAC address of the to-be-authenticated end, the information sequence is hidden in the gray image through a steganography algorithm to obtain an authentication image and send the authentication image to the authentication end; the authentication end extracts an authentication sequence from the received authentication image through the steganography algorithm according to the stored MAC address of the trusted terminal, and the to-be-authenticated end passes the authentication when the authentication sequence satisfies that the number of digital 0 and 1 is equal; after the receiving end and the sending end both pass the authentication, an information key is created through a DH key exchange protocol, and the information key is used for encrypted transmission of the video. The application improves the security of video encryption transmission.

The application discloses a kind of based on parallel confrontation's generative image steganography method, system, electronic equipment and computer readable storage medium, belong to image steganography technical field.The application proposes a kind of based on adding channel attention mechanism U-Net structure generation confrontation sample, enhance the security of multiple confrontation network architecture of existing steganography method, original image is generated by generator generation confrontation noise, and confrontation noise is reasonably distributed to original image to generate confrontation sample, and the steganography image generated by using the generated confrontation sample as carrier image through existing steganography algorithm can resist the detection of several most advanced steganalysis of present time;Solve the problem that " cannot resist the simultaneous detection of multiple steganalysis and cannot resist the detection of most advanced steganalysis of present time" in prior art.

Provided in the embodiments of the present description is an image verification method, which is executed by a steganographic device, wherein the steganographic device comprises: a photographic module and a verification module. The image verification method comprises: a photographic module capturing a first image, and using a preset steganography algorithm to add, into the first image, first information corresponding to device parameters of a steganography device; and the photographic module sending to a verification module the first image, into which the first information has been added, such that whether the first image is photographed by the photographic module is verified on the basis of the verification of the first information.

This invention relates to the field of image steganalysis defense technology, and particularly to an active image steganalysis defense method and system based on signal enhancement. The method involves adding interference noise to the image to be processed based on signal texture features to enhance the stegana signal. The enhanced image is then input into a pre-trained active image steganalysis defense network. This network restores the stegana signal features and recovers the original carrier image from the processed image through an inverse difference operation. The active image steganalysis defense network employs a dual-channel parallel network model to reconstruct the stegana signal distribution by mining the correlation and spatial relationships between the stegana signals. This invention does not require knowledge of the steganalysis algorithm type and embedding rate. Through image noise addition and neural network modeling, it achieves dual destruction of secret information in the image under a heterogeneous balance state, while simultaneously restoring the quality of the original image.

The invention discloses an image cleaning method based on fusion of multiple image processing technologies. The method comprises the specific steps that S1, an image file to be cleaned is input; s2, performing format recognition on the image file input in the step S1 by adopting a plurality of image format recognition modes, if the results obtained by recognition in the plurality of image format recognition modes are the same, determining that the format recognition is successful, and entering a step S3; otherwise, entering the step S5; s3, performing format cleaning on the image file according to the format type of the image file identified in the step S2 to obtain the image file after format cleaning; s4, performing content cleaning on the image file after format cleaning to obtain a cleaned image file; and S5, ending, and recording a cleaning log or an error log. According to the method, multiple image processing technologies are fused, the thought of highly depending on a steganography algorithm is broken, the image format cleaning thought is constructed, and the image cleaning effect and safety are comprehensively improved.

This invention discloses a method for constructing immune steganographic images based on an artificial immune system, suitable for digital image information hiding. By constructing an information hiding model based on the artificial immune system, the method automatically optimizes and modifies the secret image with the goal of reducing the distance between the residuals of the carrier image and the secret image to generate an immune steganographic image, thereby effectively enhancing the security of the steganographic algorithm. The method is universal and applicable to any steganographic method that minimizes distortion. It ensures that modifying the stegoimage does not cause secret message extraction failures and does not introduce additional detectable artifacts, further improving the security performance of existing steganographic algorithms and possessing practical value.

The invention belongs to the technical field of video encryption transmission, and particularly relates to a video transmission method and system for a multimedia video, and the method comprises the steps: carrying out the identity verification of a receiving end and a transmitting end: enabling a to-be-verified end to randomly generate a gray image and an information sequence, and enabling the information sequence to be a binary sequence, and enabling the number of numerical codes 0 to be equal to the number of numerical codes 1 to be equal; hiding the information sequence in the grayscale image through a steganography algorithm according to the MAC address of the to-be-verified end, obtaining a verification image, and sending the verification image to the verification end; the verification end extracts a verification sequence from the received verification image through a steganography algorithm according to the stored MAC address of the trusted terminal, and when the verification sequence meets the condition that the number of the numerical codes 0 is equal to the number of the numerical codes 1, the to-be-verified end passes identity verification; and after the receiving end and the sending end pass the identity verification, an information key is created through a DH key exchange protocol and is used for carrying out encrypted transmission on the video. According to the invention, the security of video encryption transmission is improved.

The application provides an image steganography detection method, device and equipment, and relates to the technical field of image processing. The image steganography detection method comprises the following steps: performing multi-dimensional feature extraction on a to-be-processed image to obtain image features in multiple dimensions; performing deep feature extraction on the image features in multiple dimensions to obtain deep features in multiple scales; fusing the deep features in multiple scales to obtain fused features; and performing classification prediction on the fused features to obtain a prediction result, which is used to represent whether the to-be-processed image is a steganography image. The application extracts multi-dimensional features to capture feature information brought by different steganography algorithms, fuses the features to strengthen the feature information of steganography in the feature matrix, and finally judges whether the image is steganography through a prediction structure. According to the method provided by the application, the to-be-processed image is detected only once, multiple steganography algorithms can be analyzed, the accuracy of image steganography detection is improved, and the computational complexity is effectively reduced.

This invention discloses a lossless steganography method based on process hooks, comprising: analyzing nodes of attack images on social media platforms and constructing an image processing link library; identifying and locating lossy functions that cause information distortion based on syntax-semantic joint features and dynamic taint tracking technology; and bypassing lossy functions using process hooks to ensure lossless transmission of secret information. Experiments show that this invention is universal, requires no modification to existing steganography algorithms, and achieves lossless extraction of secret information in a real social ecosystem for the first time, significantly enhancing the reliability of covert communication in complex adversarial environments. This invention achieves lossless steganography by bypassing lossy attacks on social media platforms through process hooks, breaking the constraints between embedding capacity, security, and robustness, allowing steganography technology to focus on simultaneously improving security and embedding capacity, providing a universal and efficient solution for achieving lossless steganography.

This invention discloses a pixel cost redistribution algorithm based on an artificial immune system for digital image information hiding. Under a model minimizing additive distortion, the algorithm adjusts the pixel modification probability distribution obtained from a given cost function and constructs an immune information hiding model to dynamically optimize the adjustment method of the pixel modification probability distribution, thereby rationally redistributing the modification cost. This concentrates modified pixels in areas of the carrier image that are difficult to detect. The method is applicable to various advanced cost functions, overcomes the problem that existing heuristic adaptive steganography algorithms, which rely heavily on empirical design, struggle to achieve optimal security, and can improve the security of state-of-the-art spatial image steganography algorithms, demonstrating practical value.

The present invention proposes a multi-channel metasurface-based optical encryption steganography method and system, relating to the field of metasurface optical encryption technology. The method mainly comprises: constructing a dual-structured non-spatial-multiplexed three-channel metasurface based on the material properties of a dual-wavelength metasurface; encoding a first meta-image used to transmit secret information extraction and recovery parameters, as well as a second meta-image and a third meta-image embedded with secret information using a run-length encoding-based steganography algorithm, into the dual-structured non-spatial-multiplexed three-channel metasurface for optical encryption, and decrypting them requires optical keys of different levels. The present invention can reduce the risk of information being easily cracked by exhaustive analysis due to a single key level, and utilizes a multi-carrier information steganography method to prevent direct observation of optically encrypted information.

This invention discloses a reinforcement learning-based method for secure text steganography and transmission in UAV communication, enabling secure transmission of control commands. Addressing the problems of easily eavesdropped and cracked control commands, weak anti-attack capabilities, and unstable communication links in existing UAV communication methods, this invention proposes a method integrating reinforcement learning and dynamic steganography generation. Through an adaptive dynamic steganography algorithm, control commands are embedded into the text. Reinforcement learning is used to optimize sentence length generation and corpus selection, ensuring the generated text's scene adaptability and resistance to steganography analysis. Simultaneously, transmission strategies are adjusted based on real-time channel conditions. Efficient parameter fine-tuning maintains the model's inference speed and low resource consumption, meeting the stringent requirements of real-time performance and stealth in UAV communication.

The invention belongs to the technical field of image processing, and discloses a real-time low-visual-degradation steganographic image purification method, which comprises the following steps of: firstly, dividing an image into a plurality of non-overlapped pixel blocks, and proposing a constraint mechanism for block pixel values based on image distortion and human vision; thirdly, based on an active steganalysis target, a multi-target steganalysis loss function PSB is provided, and the model can be guided to achieve secret information removal and low visual degradation on the premise that a steganalysis algorithm is unknown; furthermore, a lightweight secret information clearing method based on depth-first search is provided, PSB function minimization is taken as a search target, a block constraint mechanism is taken as a boundary condition, modification is carried out by taking a pixel block as a unit, and image secret information clearing is realized through global search, slight disturbance fine tuning and modification of all pixel blocks. According to the method, the image quality is kept while the secret information is removed.

The application relates to the technical field of information security, and provides an image steganography method, a program product, a storage medium and an electronic device. The image steganography method comprises the following steps: encrypting an original text in a predetermined manner, and determining first ciphertext according to an encryption result; processing the first ciphertext to obtain second ciphertext, and recording restoration information for restoring the second ciphertext into the first ciphertext; generating first target text based on the second ciphertext by using the text-to-text capability of a first large model; generating a first carrier image based on the first target text by using the text-to-image capability of a second large model; and writing the restoration information into the first carrier image by using an image steganography algorithm to obtain a first steganography image. The method uses a large model to dynamically generate a carrier image, theoretically can generate an infinite number of carrier images, thereby greatly reducing the probability that the steganography image is suspected, intercepted and recognized, and is beneficial to improving the security of information transmission.

The application discloses a steganography method and device. The method comprises the following steps: obtaining a target stego sequence corresponding to a target carrier image; obtaining detection data for representing whether secret information is embedded in the target carrier image, and calculating a gradient image corresponding to the target carrier image by using a back propagation algorithm based on the detection data; and modifying the target stego sequence based on the gradient image to obtain a target stego image. The method uses the gradient of a steganalysis analyzer to perform dithering modulation modification on the target carrier image, so that the target stego image has higher anti-steganalysis capability. Compared with existing robust steganography algorithms, the method has improved robustness and security, and can meet the requirements of security and robustness in the information steganography process.

This invention discloses a general HEVC video steganalysis method based on a dual-stream fusion network, comprising: acquiring compressed video and preprocessing the data to obtain a prediction residual difference map and an intra-frame prediction mode difference map corresponding to each I-frame, and constructing a training set together with frame-level category labels; constructing a general video steganalysis network composed of a dual-stream fusion network, a binary classifier, and a multi-classifier, wherein the dual-stream fusion network takes the two difference maps as inputs to two branches, performs preprocessing, feature extraction, and feature enhancement sequentially, and finally fuses the features to generate a fused feature map, obtains a frame-level feature vector through global average pooling, and the binary classifier outputs the determination result of whether the I-frame is a steganalysis frame; training the network using a domain adversarial training algorithm; and obtaining the video-level determination result through aggregation rules during testing. The advantages are that it can improve the detection accuracy and versatility for multiple steganalysis embedding domains and multiple steganalysis algorithms, especially showing superior performance in steganalysis embedding domain mismatch scenarios.

The application provides a JPEG image load positioning method based on ensemble learning, and specifically comprises the following steps: step 1, collecting N pieces of load-bearing JPEG images based on the same embedding position key and embedded using the same DCT domain steganography algorithm; step 2, performing Huffman decoding on all images to obtain a quantized DCT coefficient matrix; combining the coefficients at the same position of all coefficient blocks in each image to obtain 64 pieces of same-frequency subgraphs; step 3, extracting 8-dimensional residual feature vectors for the same-frequency subgraphs in step 2; step 4, extracting 72-dimensional SPAM feature vectors for the same-frequency subgraphs in step 2; step 5, splicing the 8-dimensional residual feature vectors in step 3 and the 72-dimensional SPAM feature vectors in step 4 into 80-dimensional feature vectors; step 6, using stacking combined with K-fold cross-validation to perform model fusion; and using the 80-dimensional feature vectors in step 5 to train to obtain a final model.