94 results about "Steganalysis" patented technology

The invention discloses a general steganalysis detection method for a spatial domain and a JPEG domain, which is characterized in that an SRM filtering kernel initialization branch and a randomly initialized convolution kernel branch are mutually fused through a well-designed bidirectional fusion module, the fused complementary information promotes the effective cooperation and learning of the two branches, the noise residual error extraction capability of the double branches is enhanced, and the noise residual error extraction efficiency is improved. Therefore, the signal-to-noise ratio of the preprocessing stage is improved. A multi-level feature fusion module is introduced in feature extraction, feature maps of different levels are weighted through a space attention mechanism, the feature maps of different levels are integrated by down-sampling to the same scale, and weak steganography features are reserved. In multi-scale attention pooling (MSAP) in a classifier, convolution kernels of different sizes are utilized to extract multi-scale features to enhance feature expression, then gated channel transformation (GCT) is utilized to control weight distribution among channels, and finally, style pooling of global standard deviation pooling and global average pooling is combined to perform global pooling on a feature map, so that the statistical magnitude is improved.

The invention relates to an image steganography method based on a high-frequency distribution mapping depth reversible network, and the method comprises the steps: designing a reversible steganography module and a high-frequency distribution mapping module based on an affine coupling structure, and constructing a depth reversible steganography network; combining the forward hidden network and the reverse recovery network, constructing a corresponding loss function, and training an image steganography process, an image recovery process and a high-frequency distribution mapping process for the deep reversible steganography network to obtain a trained deep reversible steganography network; and on the basis of the trained deep reversible steganography network, image steganography, high-frequency distribution mapping and image recovery processing are sequentially carried out on the current carrier image and the secret image, and a secret-carrying image and a recovered image are respectively obtained. Compared with the prior art, the method has the advantages that the high-frequency distribution mapping module is designed to improve the recovery quality of the secret image in the reversible neural network, global end-to-end image steganography is realized, and the image steganography safety, the recovery image quality and the steganalysis resistance can be effectively improved.

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 language steganalysis method based on knowledge injection and knowledge coding, and belongs to the field of information hiding. Firstly, a language steganalysis system model based on knowledge injection and knowledge coding is constructed, the system model preprocesses input sentences with the help of a knowledge graph, and knowledge injection is completed. Then, BERT is adopted for the sentence tree and the visible matrix, extraction of semantic features is completed, and knowledge injection semantic coding is carried out. During preprocessing, a KE module for graph structure modeling is used for carrying out knowledge extraction on an input sentence and a knowledge graph to obtain a mini graph, and knowledge coding is carried out on the mini graph through GAT. Finally, the captured entity node features and the representation vectors obtained from the knowledge injection module are connected in series and added into a full connection layer, and the system model predicts whether an input text is a normal carrier or a steganography carrier through output of the full connection layer. According to the method, the problem of fragmentation of web texts is solved, and the result of language steganalysis is more accurate.

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 present invention relates generally to the technological field of cyber security. More specifically, the present invention relates to preventing cyber-attacks caused by delivering steganographically encoded malware through machine-learning (ML)-based model distribution. In the general aspect, the invention may be directed to a method of preventing steganographic attacks by at least one processor. The method may include receiving an initial machine-learning (ML)-based model; applying a steganalysis procedure to the initial ML-based model, to determine a probability of steganographic data presence in the initial ML-based model; applying at least one alteration to the initial ML-based model, according to the determined probability, thereby obtaining a disarmed ML-based model.

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 invention discloses a steganography detection method and system based on multi-source domain adaptation, and relates to the technical field of information security, and the method comprises the steps: receiving data of a plurality of source domains and target domains, inputting the data of the plurality of source domains and the target domains into a pre-established public feature extractor, and extracting to obtain feature representations of the source domains and feature representations of the target domains; inputting the feature representation of the source domain and the feature representation of the target domain into a pre-established feature level mixing module for weighted mixing to obtain a mixed feature, obtaining a target domain feature, and performing loss calculation based on the target domain feature and the mixed feature to obtain a mixed class loss; and inputting the data of each source domain and the target domain into a pre-established domain feature extractor which does not share the weight, outputting to obtain domain alignment loss, inputting the mixed class loss and the domain alignment loss into a pre-established domain specific classifier, and outputting to obtain a final prediction result. And the detection precision and generalization ability of the steganography detection model under the cross-domain condition are effectively improved.

The invention belongs to the technical field of image processing, and particularly relates to an image tampering protection method based on a steganography technology, which comprises the following steps of: S1, acquiring an image, inputting associated information, marking a key area on the image to obtain an initial image, and enabling the key area to be an area containing key information; s2, segmenting the initial image into a key region image and a non-key image, inputting the key region image and the non-key image into a steganography module, respectively embedding associated information into the key region image and the non-key image by the steganography module through a steganography technology, and combining the key region image and the non-key image into a transmission image; and S3, decomposing the transmission image through a steganography analysis module to obtain steganography information respectively positioned in the key area image and the non-key image, comparing differences between the steganography information at different positions, and judging whether the transmission image is tampered or not according to a difference value and an identification result. According to the method, the associated information is secretly transmitted through the image, and whether the image is tampered or not can be better judged.

Systems, computer program products, and methods are described herein for steganalysis detection of metadata in a video stream for providing real-time data. An encrypted steganalysis marker is positioned within a boarder of a video stream and, upon user device scanning and identification provides a communication channel for real-time data display on the user device. The data displayed on the user device is transmitted via narrowband internet-of-things communication linkage displaying product or service data associated with the video stream. Using a combination of NB-IoT and computational memory computing creates a unique, in user device computing structure with various variable inputs to provide specified data and recommendations to the user.

The invention discloses an attention mechanism-based HEVC (High Efficiency Video Coding) transformation coefficient video steganalysis method, which comprises the following steps of: performing data preprocessing on a compressed video, and extracting a prediction residual image corresponding to an I frame to form a training set; then, constructing a deep neural network comprising a convolution sub-network, a self-attention sub-network and a binary classifier, wherein the deep neural network is used for extracting steganography traces and classifying videos; through training and testing, the network can accurately judge whether the video is subjected to steganography processing or not; the steganalysis method has the advantages that steganalysis is carried out in combination with the deep learning technology, the accuracy and robustness of steganalysis can be improved, the steganalysis method can adapt to diversified steganalysis methods, and high detection performance is achieved in a low-embedding-rate scene.

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 a steganalysis method and device, equipment, a medium and a product. The steganalysis method comprises the following steps: acquiring an initial to-be-recognized image; based on the three-primary color channel of the initial to-be-recognized image, performing image enhancement processing on the to-be-recognized image to obtain target feature information; and performing feature analysis on the target feature information to obtain a steganalysis result. According to the technical scheme, steganalysis for color images is realized.

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 present invention provides a method and device for training a steganalysis network by combining source supervision and target unsupervised information. The steps of the method include: inputting a test data set into the steganalysis network to output the category of each target domain image in the test data set; obtaining the parameter matrix corresponding to each source domain image in a preset training data set through a feature extraction layer, calculating the source domain feature mean matrix corresponding to each category based on the parameter matrix of the source domain image, and calculating the target domain feature mean matrix corresponding to each category based on the test data set; calculating the inter-domain average difference corresponding to each category based on the source domain feature mean matrix and the target domain feature mean matrix of each category, and calculating the value of the cross-entropy loss function; calculating the individual prediction entropy and the average prediction entropy based on the probability parameters of the target domain feature image belonging to two categories, and calculating the total prediction entropy; calculating the value of the loss function based on the value of the cross-entropy loss function and the total prediction entropy, and training the steganalysis network.

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 present invention discloses a large-capacity steganographic method for color images based on a carrier generation type based on a diffusion model, which relates to the field of image processing technology and is characterized in that it includes the following steps: S1: training a diffusion model separately, allowing the diffusion model to fully learn the image semantics of a given data set and sample images with the same distribution as the given data set, and use them as carrier images of the image steganographic part; S2: in the image steganographic part based on FusionUnet. The technical problem to be solved by the present invention is to provide a large-capacity steganographic method for color images based on a carrier generation type based on a diffusion model, embedding a secret image into a new carrier image generated by sampling a diffusion model, so that the steganographic image obtained by embedding on the generated new carrier image not only has extremely high visual quality, but also has stronger anti-steganographic analysis capability.

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.

The invention relates to the technical field of information hiding, and discloses a pitch period modulation information hiding detection method based on a Bayesian network. From the perspective of different network structures, the invention achieves the best effect by using a joint network combining three networks, namely, intra-frame, inter-frame and self-network. From the perspective of detection performance at different embedding rates, the detection accuracy of the method of the invention at a lower embedding rate is better than some existing steganalysis methods. From the perspective of the size of PDBNS, the smaller the number of Bayesian networks, the shorter the system detection time. When the number of Bayesian networks is small, the method still has a higher detection accuracy, and can realize real-time detection of the pitch period modulation information hiding method. From the perspective of detection performance at different time lengths, the method of the invention has a 100% detection rate of audio from 1 second to 10 seconds at an embedding rate of 1.0, which is better than some existing algorithms.

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.