65 results about "Steganalysis" patented technology

The invention discloses a large language model security testing method based on evolutionary dynamic adversarial attacks, and relates to the technical field of security testing. Comprising the following steps: 1, creating a dynamic attack generation engine, constructing an adversarial evolution architecture by utilizing the dynamic attack generation engine, and generating a test sample based on the adversarial evolution architecture for a large language model security test; the method comprises the following steps: 1, establishing a large-scale language model, 2, calculating investigation parameters of harmlessness, honesty and helpfulness based on a Constancy AI principle, calculating a security alignment gap index SAGI by using the investigation parameters, and intelligently judging a value drift condition of the large-scale language model according to the security alignment gap index SAGI, 3, establishing a multi-modal joint defense engine, and carrying out intelligent judgment on the value drift condition of the large-scale language model according to the value drift condition of the large-scale language model. Steganalysis, syntax tree analysis and audio anomaly detection functions are integrated, and all-around threat detection coverage of texts, codes, images and voices is carried out on a detected large language model.

The invention discloses a medical image privacy protection-oriented structure perception adaptive steganography method, which comprises the following steps of: in a training stage, generating a diagnosis related region mask based on a pre-trained IMIS-Net, extracting an embeddable suppression mask, and obtaining an initial embedding probability under the minimum distortion constraint; and the self-adaptive adjustment of structure perception is carried out on the pixel-level embedding probability in combination with a reinforcement learning normal form, so that the ROI is protected preferentially and a high-risk region is inhibited on the premise of meeting the set capacity, and the steganalysis resistance and perception quality are improved. And after the training is completed, in a reasoning stage, the masks and probabilities are firstly generated or quoted, then action probabilities are obtained through a trained strategy network, then probability consistency is mapped into STC cost, real embedding is completed by the STC, and a secret-containing medical image is obtained. According to the invention, on the premise that the availability of image diagnosis is not reduced, safe embedding and hidden transmission of sensitive information are realized.

The invention provides an image steganalysis method and device based on channel transformation and depth residual. The method comprises the following steps: constructing an image steganalysis network; constructing an image data set, wherein the image data set comprises a plurality of carrier images and secret-carrying images corresponding to the carrier images; training the image steganalysis network by using the image data set to obtain a trained image steganalysis model; inputting the to-be-analyzed image into the image steganalysis model to obtain a detection result; the detection process of the image steganalysis model comprises the following steps of: extracting high-frequency noise in an input image by adopting a space rich model filter to obtain a feature map; processing the feature map by adopting a channel attention mechanism to obtain a weighted feature map; channel transformation is carried out on the weighted feature map, then processing is carried out through a Res2Net network, and finally pyramid feature pooling operation is carried out to generate a one-dimensional feature steganography vector; based on the one-dimensional feature steganography vector, the input image is classified as a carrier image or a secret-carrying image.

The present application is a backdoor attack method based on image steganography, comprising: S1, constructing an image steganography network and an image transformation network; S2, performing spatial transformation on a poisonous image to obtain a first trigger; S3, inputting the spatially transformed poisonous image back into the attack network to restore it to obtain a second trigger; S4, constructing a steganalysis loss function of the image steganography network based on the distance loss between the first trigger and the second trigger; S5, iteratively training the image steganography network; S6, performing a backdoor attack using a poisonous image. When training the steganography network, a loss function is constructed based on the degree of deformation of the trigger, and the image steganography network generates a poisonous image that can adapt to image transformation through the back propagation of the loss function. The poisonous image generated by the present application can trigger an attack in the victim model even after image transformation processing, so that the victim model recognizes the corresponding target label.

The invention relates to the technical field of image information security, and discloses a controllable image steganography method and system based on a steganography diffusion model. Comprising the following steps: acquiring a hash value of a color plaintext image, and obtaining a plurality of random sequences according to the hash value and three-dimensional memristor chaotic mapping; decomposing the color plaintext image into three channel matrixes, and performing scrambling and diffusion operation on the channel matrixes based on a random sequence to generate a color ciphertext image; adding noise to the carrier image to obtain a preprocessed carrier image; obtaining a plurality of wavelet coefficient matrixes according to the preprocessed carrier image; generating a ciphertext-containing wavelet coefficient matrix according to the color ciphertext image and the wavelet coefficient matrix; performing inverse discrete wavelet transform on the confidential wavelet coefficient matrix to obtain a noisy and confidential carrier image; and de-noising the noisy and confidential carrier image by using the steganography diffusion model to generate a final confidential carrier image. According to the method, the visual quality of the carrier image can be ensured, steganography analysis is effectively resisted, and the image steganography safety is improved.

The present invention discloses an end-to-end method for detecting adversarial steganography. This method addresses the problem that adding adversarial perturbations to steganography methods makes it difficult for steganalysis models to detect adversarial steganography. The method utilizes confidence artifacts and pixel artifacts to detect adversarial steganographic images. By designing a confidence loss function to amplify the confidence artifacts caused by the adversarial perturbation, more easily detectable gradient flow information is obtained, which is then input into a designed gradient subnet for feature extraction. Simultaneously, the image is divided into two sub-blocks and scrambled up and down to destroy the adversarial perturbation. The feature similarity between the two image sub-blocks is used to reduce the classification difference between the adversarial perturbation and the stegoscopy noise, highlighting its difference from natural noise, thereby reducing the negative impact of the adversarial perturbation. The method of the present invention is simple to calculate and easy to implement. It can effectively improve the detection accuracy of adversarial steganography and has practical value.

The invention discloses an image steganalysis method based on a reversible neural network and electronic equipment. The method comprises the following steps: inputting a carrier image and a secret image into a multilayer reversible neural network, and processing an initial steganographic image output by the multilayer reversible neural network by adopting a residual augmentation strategy to obtain a final steganographic image; the final steganographic image and the carrier image are matched with random Gaussian noise to be input into a multilayer reversible neural network for reverse recovery, and a recovered secret image and a recovered carrier image are output; calculating an overall loss function, and updating parameters of the multilayer reversible neural network; repeating the above steps until the multilayer reversible neural network converges; and inputting the to-be-analyzed image and the random Gaussian noise into the multilayer reversible neural network, outputting a recovered image by the multilayer reversible neural network, and judging whether the to-be-analyzed image is a steganographic image or not according to the recovered image. According to the method, whether the to-be-analyzed image is the steganographic image or not can be judged, hidden information in the steganographic image can be recovered, and generalization ability is improved.

The present application relates to the technical field of information security, in particular to a sensitive information detection technology and system based on multi-modal and steganographic detection, which first verifies the real type of a file by using file header information and binary data analysis through a format tampering prevention module to prevent format camouflage attacks; a sensitive data detection module adopts DistilBERT and LSTM structure to pre-process text, extract text embedding, and mine text features through bidirectional LSTM and Attention mechanism to finally accurately determine whether the text contains sensitive information; a joint content and file steganographic detection module fuses convolutional neural network and multi-modal learning, uses corresponding models for steganographic information detection according to different data types, integrates results through multi-modal neural network, and uses multi-modal feedback and cross-modal reasoning mechanism to improve detection accuracy; the present application effectively enhances the accuracy, comprehensiveness and system adaptability of sensitive information detection.

The application discloses a satellite quantum communication method and system based on steganography, wherein the method comprises the following steps: a satellite ground TT&C station implants relevant information into a carrier file in the form of information steganography and uploads the carrier file to a pre-agreed website; when a satellite flies over, an encrypted communication instruction is sent to a satellite quantum communication load; the satellite quantum communication load emits quantum light and points the quantum light to a corresponding satellite quantum communication ground station through a transceiver telescope module; the satellite quantum communication ground station determines whether to check the pre-agreed website according to weather conditions, if the weather conditions meet the quantum communication, the satellite quantum communication ground station acquires relevant information of the satellite quantum communication task through the website and performs steganographic analysis; and according to the steganographic analysis result, the satellite quantum communication ground station starts a receiving telescope module at a specific time, points and tracks the satellite, receives quantum light and processes the quantum light to generate a quantum key. The application can avoid direct communication between the ground station and the TT&C station, and protect the geographical position information of the ground station.

The invention discloses a lightweight image steganalysis method based on deep reinforcement learning pruning, which optimizes the training process of a steganalysis model through dynamic pruning and realizes collaborative optimization of model lightweight and performance improvement. In the training process of the steganalysis model, a reinforcement learning agent is periodically utilized to perceive the model state and decide the pruning rate of each layer, a state vector containing sparseness, the proportion of residual parameter quantities and the model accuracy is constructed, a pruning strategy is selected through a strategy set, and a benefit value is calculated. The method can effectively compress the model parameter quantity, improve the reasoning efficiency and reduce the deployment cost on the premise of ensuring that the model steganalysis accuracy is not obviously reduced, is suitable for an image steganalysis task in a computing power limited scene, and meets the requirement of model lightweight.

The present invention discloses a language steganalysis method based on knowledge injection and knowledge encoding, which belongs to the field of information hiding. First, a language steganalysis system model based on knowledge injection and knowledge encoding is constructed. The system model preprocesses the input sentence with the help of a knowledge graph to complete the knowledge injection. Then, BERT is used on the sentence tree and the visible matrix to complete the extraction of semantic features and perform knowledge injection semantic encoding. During the preprocessing, a KE module of graph structure modeling is used to extract knowledge from the input sentence and the knowledge graph to obtain a mini-graph, and the mini-graph is knowledge encoded by GAT. Finally, the captured entity node features and the representation vector obtained from the knowledge injection module are connected in series and added to a fully connected layer. The system model predicts whether the input text is a normal carrier or a steganographic carrier through the output of the fully connected layer. The present invention solves the fragmentation problem of network text and makes the results of language steganalysis more accurate.

The invention discloses a general image steganalysis method, electronic equipment and a medium, and the method comprises the steps: inputting a to-be-detected image into a trained general image steganalysis model, and obtaining a prediction probability that the to-be-detected image is a carrier image or a steganography image; wherein the process of processing the to-be-detected image by the trained general image steganalysis model comprises the following steps of: preprocessing the to-be-detected image based on a filter bank of a forked structure, and activating and outputting an enhanced noise characteristic pattern through a hyperbolic tangent function; carrying out convolution processing on the enhanced noise feature map to obtain a local noise feature map; performing channel weighting, feature expansion and convolution visual transformation processing on the local noise feature map to obtain a global noise feature map; and alternately processing the global noise feature map through a full connection layer and a leakage correction linear unit, and outputting the prediction probability of a carrier image or a steganographic image through a classifier.

The invention discloses a quantization index modulation steganography method based on chaos assistance, and aims to improve the security and concealment of information hiding. According to the method, a high-complexity pseudo-random sequence is generated by combining a delay coupling Lorentz chaotic system, the pseudo-random sequence is converted into a uniformly-distributed sequence through modular lattice operation, the uniformly-distributed sequence serves as external jitter to be introduced into a lattice quantization process, and therefore safe embedding of messages is achieved. According to the method, high-density embedding can be realized while carrier signal statistical characteristics are kept to effectively resist statistical steganalysis, and the method has relatively good image quality and robustness and is suitable for various types of digital carriers such as images, audios and the like.

The application provides a general image tampering forensics method and system based on multi-scale feature fusion, which can not only detect various tampering technologies, but also has strong robustness, efficient and accurate extraction and identification of image features, and fast image authentication speed. The method comprises the following steps: step 1, preprocessing the image data to be tampered with to enhance the tampering effect; step 2, extracting the local information of the preprocessed image by using the rich model in steganalysis, and combining the multi-scale local information guided by the attention mechanism; step 3, extracting the overall semantic features of the image by using the image classification model; step 4, fusing the local information and the semantic features by using the bilinear pooling; step 5, inputting the fused features into the attention map of the spatial domain to learn the positioning of the image tampering area; and step 6, inputting the fused features into the full connection network of the classification to evaluate the image tampering forensics and determine whether the image is tampered with.

The present invention discloses a motion vector domain video steganalysis method based on skipped macroblocks, comprising: extracting first-category features of the skipped macroblocks by employing a recompression calibration method to obtain predicted motion vector recovery features of the skipped macroblocks; extracting second-category features of the skipped macroblocks by employing a recompression calibration method to obtain state transition probability features of the skipped macroblocks; and merging features by merging the predicted motion vector recovery features and state transition probability features of the skipped macroblocks to obtain comprehensive video steganalysis features of the skipped macroblocks. The motion vector domain video steganalysis method based on skipped macroblocks of the present invention extracts and merges predicted motion vector recovery features and state transition probability features of the skipped macroblocks through recompression calibration to improve the correct detection rate of steganalysis.

This application provides a text steganography analysis method, apparatus, device, medium, and program product, which can be applied to the field of text detection. The method includes: in response to receiving text to be detected, inputting a first feature vector of the text to be detected into a semantic baseline extraction layer of an analysis model, extracting low-frequency semantic features and artifact dimension signal features from the first feature vector, and fusing the two to obtain a second feature vector; inputting the first feature vector into an artifact feature amplification layer to amplify the artifact dimension signal features in the first feature vector, obtaining a third feature vector including the amplified artifact dimension signal features; inputting the second feature vector and the third feature vector into a global representation extraction layer for feature fusion to obtain a fourth feature vector; inputting the fourth feature vector into a classification head module to obtain a detection result, the detection result including whether the text to be detected is normal text or steganographic text.

This invention discloses an image steganography method, apparatus, electronic device, and storage medium. The method designs a Res-SS2D module, which, through four-way global spatial modeling, improves the quality of the secret image while maintaining linear complexity. A high-frequency feature enhancement strategy is proposed. By extracting the edges of the carrier image and fusing them into the encoder, the secret information is preferentially embedded in high-frequency regions, reducing the likelihood of detection by steganalysis. A multi-objective loss function is designed, combining PSNR and MS-SSIM to optimize generation quality. The L1 norm loss of the low-frequency components is used to constrain the low-frequency consistency between the carrier and the secret image, ensuring that the secret information is preferentially hidden in the high-frequency components. This invention provides an efficient and secure solution for image steganography.

The application provides a steganalysis method for carrier selection steganography, comprising: acquiring a to-be-detected image and converting it into a grayscale image; constructing a corresponding gray level co-occurrence matrix in multiple directions to capture the texture features of the image; extracting corresponding texture features based on the gray level co-occurrence matrix and performing weighted summation on the texture features in different directions by using a multi-scale feature fusion method to obtain a texture complexity value of the to-be-detected image. A training set including a carrier image set and a stego image set is used to train a classifier; the to-be-detected image is input into the trained classifier to obtain a probability value. According to the texture complexity value, the probability value output by the classifier is dynamically adjusted, and a preset judgment rule is used to judge whether the to-be-detected image is a stego image. The method is suitable for steganalysis based on manually designed features and steganalysis based on deep learning, can effectively identify stego images in carrier selection steganography, and improves the accuracy and reliability of steganalysis.

The application belongs to the technical field of information hiding in information transmission, and discloses a steganographic text anti-steganalysis capability enhancement method based on deep learning. The method learns the characteristics of the data set in the open domain environment by using the deep learning method, generates steganographic text with the statistical distribution of the original data set while embedding secret information, and finally recommends the corresponding emoticons for the steganographic text according to the distribution of the emotional tendency and emoticons of the steganographic text in the real environment, so as to further strengthen the anti-steganalysis capability of the steganographic text. The application solves the problem of missing emotional clues in the generated text steganography, so that the anti-attack capability of the steganographic text is stronger when the steganographic text is transmitted in the public channel.

This invention discloses an image steganography method and system based on the NICE model. The method comprises: constructing the NICE model, acquiring an image dataset to train the NICE model; after training, reversing the generator in the NICE model to obtain an extractor; directly generating a secret image from a secret message using the generator; and extracting the secret message from the secret image using the extractor to retrieve the secret message. The images generated by this method fully match the data distribution of real images, making it difficult for machines and humans to verify their authenticity. Therefore, the secret images generated by the model cannot be detected by steganalysis tools based on probabilistic statistics, making them theoretically more secure.

The present invention relates to an image steganalysis method and apparatus, comprising the following steps: obtaining image information to be classified, dividing the image to be classified into n image blocks of equal size, where n ≥ 3; preprocessing each image block to obtain residual information for each image block; extracting a feature vector from the residual information of each image block; pairing the n image blocks according to texture complexity, calculating the distance between the feature vectors of each pair of image blocks to form an image block feature distance group; fusing the feature vectors of the n image blocks with the image block feature distance group to generate a fused feature vector; converting the fused feature vector into a two-dimensional feature vector, wherein the two-dimensional feature vector corresponds to the classification probability of the carrier image and the secret image; and determining the type of the image to be classified based on the two-dimensional feature vector. The image steganalysis method and apparatus of the present invention can increase the difference in steganographic signal characteristics between image blocks and has the advantage of high accuracy.

The application discloses an image spatial domain steganalysis system and method, relates to the technical field of image steganalysis, and comprises a CDNet model module, wherein the CDNet model module is composed of a feature extraction unit, a feature analysis unit and a classification unit; the feature extraction unit carries out pretreatment on an input image through an SRM high-pass filter, embeds a light channel attention mechanism into a residual structure, and constructs a residual channel attention block structure; four stacked composite down-sampling blocks of the feature analysis unit are jointly composed of a normal convolution, a detail enhancement convolution and an average pooling layer; the classification unit converts feature maps obtained through the preceding modules into a feature vector through a global covariance pooling layer, and outputs a classification result through a full connection layer and a softmax function, effectively enhances noise residual features extracted by a pretreatment layer, effectively reduces the loss of steganographic signals in a down-sampling process, and guarantees detection accuracy.

The application discloses a steganalysis method based on a multi-scale residual network, and relates to the technical field of image steganalysis, and solves the technical problem that in the prior art, a single-channel group is difficult to adapt to different image natural characteristics and steganographic feature distribution differences, weak feature signals are easily lost, and detection effect is limited. By introducing a same-layer multi-scale receptive field mechanism and a same-layer mixed feature channel group mechanism, a multi-scale receptive field network model is constructed. The multi-scale receptive field network model selects multiple different scales of receptive fields in the same layer, generates a same-layer mixed feature channel group, and is subjected to interval mixed identification. Different feature scale channel groups are fused, and MRNet is optimized, so that steganographic features are extracted and detected by using multi-range receptive fields. The problem of feature loss of weak feature signals when steganographic image information is input into a neural network and in the process of transmission of weak features from a low layer to a high layer of the network is solved.

The application discloses a kind of based on reversible neural network's image steganalysis method and electronic equipment.The method includes: carrier image and secret image are input into multilayer reversible neural network, initial steganographic image output by multilayer reversible neural network is handled using residual augmented strategy, obtain final steganographic image;Final steganographic image, carrier image are respectively with random Gaussian noise input multilayer reversible neural network and are recovered reversely, output recovered secret image, carrier image;Calculate overall loss function, update multilayer reversible neural network parameter;Repeat the above steps until multilayer reversible neural network converges;With the image to be analyzed, random Gaussian noise is input into multilayer reversible neural network, and multilayer reversible neural network outputs recovery image, whether the image to be analyzed is steganographic image according to recovery image is judged.The application can judge whether the image to be analyzed is steganographic image, can also recover hidden information in steganographic image, and further improve generalization ability.

Existing steganography techniques generate pseudo-sequences that retain only a small portion of the statistical distribution features of the original DNA sequence, resulting in poor concealment and weak resistance to steganalysis, thus leading to low security. This invention proposes a sequence-generating DNA steganography method based on conditional probability adaptive coding. First, an RNN network is used to learn the statistical distribution features of natural DNA. Then, an adaptive grouping algorithm generates pseudo-sequences highly similar to the original DNA. Experimental results show that this method significantly improves the concealment and resistance to steganalysis of the pseudo-sequences, enhancing the security of information hiding. Furthermore, given the lack of a comprehensive evaluation mechanism for the concealment of pseudo-sequences generated by existing DNA steganography techniques, this invention proposes using three parameters to evaluate the perceptual and statistical concealment of the pseudo-sequences, further refining the evaluation system for the security of DNA steganography systems.

The invention discloses a robust audio steganography method and system for resisting social network channel transcoding, and belongs to the technical field of information security. The method comprises the following steps: generating a carrier bit sequence based on a QMDCT coefficient set of an original audio; obtaining a cost sequence based on the modification cost of each bit in the carrier bit sequence; generating an error correction coding result of the secret information to be embedded; synchronously scrambling the carrier bit sequence and the cost sequence to obtain an out-of-order carrier bit sequence and an out-of-order cost sequence; searching a path with the minimum total modification cost in a grid chart defined by the out-of-order carrier bit sequence and the error correction coding result based on the out-of-order cost sequence to obtain an out-of-order secret-carrying bit sequence; and generating a secret-carrying audio based on the out-of-order secret-carrying bit sequence. According to the method, on the premise of not sacrificing excessive embedding capacity, an extremely low bit error rate and excellent imperceptibility and steganalysis resistance can still be kept in a lossy channel.

The invention discloses a color image universal domain steganalysis method and system, and relates to the technical field of image steganalysis, and the method comprises the steps: constructing an exclusive dual-domain steganalysis data set of a spatial domain and a JPEG domain from an Alaka II data set, training based on an original DRNet model by adopting an SGD optimizer of a domain personalized parameter, and executing progressive transfer learning on a low-embedding-rate image, a final model is selected according to an exclusive rule to complete double-domain steganalysis; the DRNet model comprises 62 high-pass filter preprocessing layers, a main path-branch parallel block and a classification module, and the main path and the branch realize feature fusion through multiplication, addition and addition. According to the method, universal domain steganography analysis of the color image is realized, the low-embedding-rate detection precision is improved, the detection accuracy and the AUC value are remarkably superior to those of an existing model, and the method is adaptive to the actual internet color image steganography detection requirement.

The invention discloses an image steganalysis method based on a multi-scale residual network, relates to the technical field of image steganalysis, and solves the problems that in existing image steganalysis, single-channel group extraction features are difficult to adapt to natural features and steganalysis feature distribution differences of different images, weak feature signals are easy to lose, and the image steganalysis accuracy is low. And the detection effect is limited. According to the method, a multi-scale receptive field network model is constructed by introducing a same-layer multiple different-scale receptive field mechanism and a same-layer mixed feature channel group mechanism, the multi-scale receptive field network model generates a same-layer mixed feature channel group by selecting multiple different-scale receptive fields on the same layer, and the multi-scale receptive field network model is subjected to interval mixed identification, so that the multi-scale receptive field network model is obtained. Different feature scale channel groups are fused, and MRNet is optimized, so that multi-range receptive field collaborative extraction and detection of steganography features are realized; the problem of feature loss of weak feature signals and weak features in the process of transmitting the weak features from a low layer to a high layer when steganography image information is input into a neural network is solved.

This invention provides a method, system, and medium for cross-dimensional image-video steganography. The method comprises: segmenting a secret image into K secret image blocks; inputting the preprocessed secret image blocks and carrier video frames into a MemoryBank module, selecting K optimal carrier video frames for information hiding based on feature matching, and ensuring that no duplicates are selected between frames; inputting the K secret image blocks and the selected carrier video frames into a VSHI-Net model for steganalysis processing, generating an output video frame containing the secret image blocks; and using the video frame containing the secret image block as one of the inputs to the VSHI-Net model. After network processing, the recovered secret image blocks are generated, and the recovered secret image is obtained through image splicing and reassembly. This invention achieves efficient cross-dimensional embedding and lossless recovery of the secret image into the carrier video, improving the carrier utilization, concealment, and robustness of cross-modal steganography.