38 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 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 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 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 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 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.

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 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.

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.

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, in particular to a quantum generative adversarial network steganography system based on double discriminators and an information hiding method. The system comprises a quantum generator G theta theta, a distribution discriminator and a steganalysis discriminator S. Each layer of the quantum generator G theta theta is sequentially provided with an Rx rotating door, an Ry rotating door and an Rz rotating door to form a complete single-bit rotating degree of freedom, the door sequence adopts Rz (theta z) * Ry (theta y) * Rx (theta x), Rx regulates amplitude distribution, Ry gives consideration to amplitude and phase coupling, Rz carries out fine phase calibration, and Rz carries out steganalysis. An entanglement layer adopts hybrid topology, an annular controlled NOT gate CNOT is applied to each layer, and interval controlled NOT gates CNOT are additionally applied to even layers. According to the quantum steganography system, collaborative improvement of the quantum steganography system in three dimensions of distribution fitting precision, steganography safety and training stability is realized.

The invention discloses a few-sample image carrier-free steganography method based on adaptive local feature fusion, and relates to the field of artificial intelligence and information security. In order to solve the technical problems that samples are scarce in a secret-related scene and a traditional steganography method depends on large-scale data training, an end-to-end framework comprising a generator, a global consistency recovery module and a discriminator is constructed. The generator realizes precise fusion of secret features and reference features through a self-adaptive local feature fusion module; the recovery module integrates multi-scale grouping dilated convolution and linear attention enhancement to improve the recovery quality of the secret image; the discriminator adopts a spectrum normalization residual network and double-branch output, so that the steganalysis resistance is enhanced; and collaborative optimization of each module is realized through a multi-task joint optimization loss function. According to the method, under the condition of few samples, the visual quality of the steganographic image, the restoration precision of the secret image and the anti-steganalysis capability are remarkably improved, and the method is suitable for secret information hidden transmission of secret-related scenes such as national defense and finance.

The application discloses a kind of Jpeg steganalysis method and system, wherein method steps include: S1. a kind of network model DCANet for Jpeg steganalysis is constructed;S2. prepare data set, divide into training set, verification set and test set;S3. train DCANet, with the data with label is sent into model, with the way of gradient descent is adjusted parameter, so that loss function value is minimum;S4. using the network model trained to test the image to be measured, determine whether it contains secret information.The Jpeg steganalysis network proposed in the application combines the advantages of different networks, which can focus on the area with complex texture that may contain secret information, and achieves better detection results compared to typical Jpeg steganalysis methods.

The invention provides a multi-domain embedded video steganography method and system based on a PU segmentation mode and a motion vector, and belongs to the technical field of video steganography. The PU segmentation mode is modified firstly, and then the motion vector is modified, so that mutual influence of two modification domains is avoided; double embedding is carried out by using two relatively independent carrier spaces of a segmentation mode domain and a motion vector domain in a P frame, and the hiding capacity of information is greatly improved on the premise that the size of a video code stream is not obviously increased. By using the two video coding syntax elements, the available carrier space of the embedded information is increased, the modification disturbance is dispersed to different features, and the steganography security is guaranteed. The embedding operations of the two domains are mutually independent and are both based on their respective statistical characteristics and distortion functions, so that the embedding modification is more in line with the coding law, and the disturbance of the embedding operations on the statistical characteristics of the video is effectively reduced, thereby enhancing the imperceptibility of hidden information and improving the steganalysis and detection resistance.

The invention discloses a steganography-based satellite quantum communication method and system, and the method comprises the steps: enabling a satellite ground measurement and control station to implant related information into a carrier file in an information steganography manner, and uploading the related information to a preset 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 to the corresponding satellite quantum communication ground station through the transceiving telescope module; the satellite quantum communication ground station judges whether to view a pre-agreed website according to the weather condition, and if the weather condition accords with quantum communication, relevant information of a satellite quantum communication task is obtained through the website and steganalysis is carried out; and the receiving telescope module is started at a specific time according to a steganalysis result, the satellite is pointed and tracked, and the quantum light is received and processed to generate a quantum key. Direct communication between the ground station and the measurement and control station can be avoided, and geographical location information of the ground station is protected.

The application discloses a cross-corpus cross-algorithm generative text steganalysis method, which comprises the following steps: inputting a text segment into a pre-established and trained text steganalysis detection model to realize discrimination of natural text and steganographic text; the text steganalysis detection model comprises a word importance semantic coding module, a word correlation multi-scale perception module and a ridge regression classification model connected in sequence; the word importance semantic coding module is used for extracting semantic features of the text segment; the word correlation multi-scale perception module is used for acquiring word correlation features from the semantic features; and the ridge regression classification model is used for realizing discrimination of natural text and steganographic text according to the word correlation features. The method of the application can still maintain accurate discrimination of natural text and steganographic text in a small sample scene, and the performance index is higher than that of the prior art.

The invention discloses a constructive information hiding method based on a diffusion model and style migration, and the method comprises the steps: mapping secret information to be hidden into content noise based on a preset mapping rule; de-noising the content noise by using a diffusion model to obtain a content image, and encoding the content image by using the diffusion model to obtain content image attention features; encoding the style image by using a diffusion model to obtain style noise and style image attention features; and fusing the style noise and the content noise to generate initial noise, denoising the initial noise through a diffusion model, respectively injecting corresponding attention features to generate a secret-containing image, then injecting the corresponding attention features to restore the initial noise, and calculating the content noise for restoring the initial noise by utilizing inverse fusion. Secret information of content noise is extracted through an inverse mapping rule; according to the method, the problems of insufficient secret-containing image quality and insufficient steganalysis resistance in the current information hiding field are solved.

This invention discloses a GCN-based method for detecting stegchmakers in social networks, addressing the challenge of stegchmaker detection in these networks. First, a social network graph is constructed, with active users and comments designated as nodes, and user replies as directed edges with attributes (interaction frequency, content similarity, and reply time) to analyze social interactions and potential steganographic behavior. A personalized PageRank algorithm is employed, using known stegchmaker nodes as seeds to construct vectors. Weights are adjusted based on edge attributes, and the PageRank value of each node is iteratively calculated to determine the association between the node and the stegchmaker. Simultaneously, GCN is utilized to capture syntactic and semantic connections between words, and features are processed using ResNet to fuse local and global information, optimizing the understanding and processing capabilities for complex graph structures. Experiments determine the weights of text feature scores and PageRank values, comparing the performance of different methods on Reddit and Twitter datasets. This verifies that the proposed method effectively captures steganographic behavior and improves detection accuracy under different loads and encoding strategies, providing a new approach and technical support for steganalysis in social networks.

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 application discloses a non-embedded steganography method based on picture semantic mixing, and has the characteristics that it comprises the following steps: 1. Through mapping, scaling and other operations, the binary secret message S is converted into independent picture carrier pixels and is shuffled into a carrier image with a cover layer; 2. A reference image with a cover layer is randomly sampled from a reference picture; 3. The carrier image is combined with the cover layer to obtain a semantic mixed image with multiple cover layers; 4. A generator and a discriminator are constructed; 5. A loss function is constructed, and the model is trained using the gradient descent method. The embedding scheme of the application is based on the conversion mapping of the secret message and the pixel, the message and the picture are semantically fused, and the generative adversarial network is trained, so that the application has the advantages of high information embedding rate, strong anti-steganalysis ability and wide practical application scene.

The application relates to the field of content security in cyberspace security, and discloses an anti-cropping image steganography method based on a generative adversarial network, wherein a coding-decoding network is constructed by using a GAN network and a DenseNet connection, and various noises are added to a model training process to improve the robustness of the model. The application constructs a coding-decoding network based on a GAN network and a DenseNet connection, adds noise training to the model, simulates noise attacks encountered in a real transmission process, improves the robustness of a stego image, and improves the decoding accuracy of a decoder; when the stego image is cropped, the decoder still has a higher possibility of restoring complete hidden information; meanwhile, a larger steganography capacity can be maintained, which is 3-6 times the capacity of 0.2 bpp-0.4 bpp of a traditional method, and the image quality is high, and the steganography has good concealment for general steganalysis tools.