45 results about "Image steganography" patented technology

The invention discloses a generative image steganography method and system based on a diffusion model and face key points, and relates to the technical field of image steganography, and the method comprises the steps: inputting secret information needing to be hidden into a pre-trained face key point generative adversarial network, and automatically selecting face key points based on a face key point selection coding strategy, the secret information is coded into the face key points, and a face key point image of the coded secret information is generated; inputting the face key point image of the coded secret information into a diffusion model as a condition, and generating a secret-carrying face image corresponding to the face key point image by guiding the diffusion model; obtaining a secret-carrying face image, and extracting face key points in the secret-carrying face image by using a Dlib model; and inputting the extracted face key points into a face key point generative adversarial network, selecting a coding strategy based on the face key points, and recovering secret information through an inverse process of secret information coding. According to the invention, the detection resistance and safety of the steganography system can be improved.

The invention discloses a generative image steganography method and device based on Stable Diffusion and discrete wavelet transform, and the method comprises the following steps: S1, sampling a potential representation of a to-be-generated image through employing a Stable Diffusion diffusion model, embedding secret information into a potential space of the diffusion model, processing the potential representation through employing discrete wavelet transform to carry out frequency domain information modulation, and carrying out the frequency domain information modulation; obtaining the potential representation after the secret information is embedded; s2, inputting the potential representation embedded with the secret information into a diffusion model, and generating a visual natural steganographic image through a diffusion inversion process; s3, inputting the received steganographic image into a diffusion model, and decrypting the received steganographic image without original model weight modification and empty prompt conditions to obtain embedded secret information; according to the method, the frequency domain embedding technology is combined with the potential diffusion model, so that high-fidelity, high-capacity and high-robustness image steganography is realized.

The invention discloses a cross-modal generative image steganography method based on a diffusion model, which comprises the following steps of: firstly, constructing and pre-training a reversible diffusion model, a stable diffusion model and a variational auto-encoder VAE, and deploying the reversible diffusion model and the pre-trained stable diffusion model to a sender and a receiver; deploying a VAE decoder in a variational auto-encoder VAE to a sender, and deploying a VAE encoder to a receiver; a sender maps a secret image into Gaussian potential representation by adopting a denoising diffusion model, inputs a stable diffusion model and a VAE decoder to generate a steganographic image under the prompt of a text key, and then sends the steganographic image and an encrypted text key to a receiver; and the receiver decrypts the text key, maps the steganographic image into a potential representation under the prompt of the text key by adopting a VAE encoder and a stable diffusion model, and recovers the secret image by adopting a reversible diffusion model after extracting the potential representation of the secret image from the steganographic image. According to the method, cross-modal image steganography can be realized on the basis of completely controlling the content of the steganography image.

The invention discloses a medical image steganography method and system based on a reversible deep network, and aims to solve the defects that in the prior art, the steganography capacity is limited, and a multi-modal medical image scene is difficult to adapt. The method designed by the invention comprises the following steps: receiving an original medical image and secret information to be embedded; normalization and spatial alignment are carried out on the medical image; inputting a multi-scale feature extraction network, extracting low-layer texture features, middle-layer edge features and high-layer semantic features, and splicing in channel dimensions to form fusion features; the fusion feature and the encrypted secret information are spliced and then input into a reversible neural network encoder to generate a steganographic image; reversely extracting fusion features and encrypted information through a reversible neural network decoder, and reconstructing an original medical image and secret information; optimizing the network performance by adopting a structure-aware joint loss function; the method can be applied to safe storage of medical images, multi-center cooperation and medical block chain evidence storage.

The invention relates to the technical field of image encryption, and discloses an image steganography method and system based on dynamic latent variables and frequency domain embedding, and the method comprises the steps: obtaining a hash value and a plurality of secret keys of a to-be-encrypted color plaintext image; constructing a hyperchaotic system, and iteratively generating a plurality of key streams; a to-be-encrypted color plaintext image is decomposed into R, G and B channel matrixes, and three ciphertext sequences are obtained based on the three channel matrixes; the method comprises the following steps: initializing a noise image, determining a time step by using a secret key, and executing reverse denoising of a denoising diffusion implicit model (DDIM) with a fixed step length to obtain an intermediate latent variable; based on the intermediate latent variable and the three ciphertext sequences, obtaining a dense frequency domain-containing sub-band, and performing inverse discrete wavelet transform on the low-frequency sub-band and the dense frequency domain-containing sub-band to obtain a dense latent variable; and executing DDIM denoising of the time step by taking the secret-containing latent variable as a starting point to obtain a secret-containing image. By adopting the method, the security and visual quality of image steganography can be improved, and high embedding capacity is ensured.

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.

This invention relates to an adaptive image steganography transmission and reception method, belonging to the field of image steganography technology. Before steganography using a steganography model, the invention first expands the image to be steganized (the secret image) and the carrier image according to the input size of the steganography model, making them a square with the same size as the input size of the steganography model. The excess parts are then filled with a solid color. The images are then segmented and scrambled before being input into the steganography model for processing. Finally, the outputs of the steganography model are merged, and the excess parts are cropped. Similarly, during reception, the image is processed in the same way. Therefore, the steganography model of this invention can support images of various sizes, ensuring the security and invisibility of the steganography model while improving its practicality and generalization ability; it also avoids the problem of poor steganography image quality due to image size mismatch.

The invention discloses a self-adaptive asymmetric image steganography method and system based on a diffusion model, and relates to the field of artificial intelligence and information security. According to the method, the corresponding cumulative distribution probability is calculated according to the noise to be embedded, then the secret information is embedded by slightly modifying the cumulative distribution probability, and the Gaussian noise after the information is embedded is obtained by using the inverse cumulative distribution function, so that the probability distribution of the embedded Gaussian noise is kept unchanged. Before actual embedding, simulation embedding is carried out in noise to be embedded, processes of simulation embedding + 1 and-1 are respectively completed to eliminate pixel points of which information cannot be correctly extracted, and modifications introduced by embedding + 1 and-1 to the noise are calculated. And then the asymmetric steganography embedding cost of each pixel point is obtained through calculation according to the modification amount, then secret information is embedded into cumulative distribution of Gaussian noise, and finally a reverse process is completed and a secret-carrying image is generated and obtained. The problem that an existing steganography method is insufficient in detection resistance and imperceptibility is solved.

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

The invention belongs to the crossing field of information hiding and digital image processing, and particularly relates to an image steganography method and system based on frequency domain transformation and a reversible neural network, and the system comprises a wavelet transformation module which is used for executing frequency domain decomposition of a carrier image, a secret image and a steganography image, the inverse wavelet transform module is used for executing reconstruction from frequency domain features to pixel domain images, the reversible network is used for realizing reversible conversion of 24-channel fusion features, the reversible network is formed by sequentially connecting 16 GlowBlocks in series, and each GlowBlock comprises an activation standardization layer, a reversible 1 * 1 convolution layer and an affine coupling layer which are cascaded. According to the method, through the cooperation of the frequency domain feature hierarchical modeling and the reversible network, while the steganography reversibility is ensured, the visual difference between the steganography and the original carrier is remarkably reduced, the recovery precision of secret information is improved, and the method is suitable for image steganography scenes with high requirements for reversibility, concealment and recovery quality.

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

The invention discloses a generative image steganography method based on robust position mapping. According to the technical scheme, a steganography system composed of a run length coding module, an initial Gaussian noise generation module, a candidate pool processing module, a pseudorandom sequence generation module, an index sorting module, an information embedding module and a stable diffusion model SD is constructed. The run length coding module carries out run length coding on the secret information to obtain a two-tuple Y; an initial Gaussian noise generation module generates a Gaussian noise tensor zinit; the candidate pool processing module performs positive and negative value division on the zinit; the pseudo-random sequence module generates a random sequence P, and an index matrix S'is obtained through the index sorting module. The information embedding module embeds the secret sequence after run length coding in the Y into an empty tensor zemed to obtain a tensor zemed conforming to Gaussian distribution; and the SD carries out denoising on the zzzled and decodes the zzzled to generate a steganographic image. According to the method, the secret information extraction accuracy and steganography capacity can be effectively improved.

This invention discloses a reversible image steganography method based on multi-scale feature enhancement and dynamic attention. First, discrete wavelet transform is performed on the carrier image and the secret image to obtain multi-channel wavelet domain features. Then, these features are input into multiple parameter-shared reversible hidden blocks. During the coupled transform process, a multi-scale spatial information enhancement module and a dynamic attention feature refiner module are introduced to extract local and global contextual information and dynamically adjust the importance of channel and spatial features, achieving high-quality embedding of the secret information. Finally, the cryptic image is obtained through inverse wavelet transform. In the inverse recovery stage, the cryptic image and auxiliary variables are input into a symmetrical reversible recovery path to separate and reconstruct the recovered secret image and the recovered carrier image. This invention achieves near-lossless recovery of the secret information while maintaining high visual quality, significantly improving the concealment and recovery accuracy of steganography.

The application discloses a kind of based on image space random distribution lossless secret image steganography and extraction method, specifically is: using space technology in carrier image pixel low 4 place write secret information, the pixel value of secret image is using random sample to break up pixel order before embedding carrier image, for the embedding position of secret image in carrier image, using key generation random uniform distribution pixel position;Image extraction method, 16 bits character is extracted from the end of key, decode as the width and height value of secret image, through key generation secret image pixel position random sample and secret information embedding position in carrier image, extract secret information from embedding position, and restore each pixel of secret image, generate the secret image after restoration.The beneficial effects of the present application are: the present application can be used for covert information and the like Hidden transmission, with lossless extraction, embedding capacity is larger, information security is good, not easy to detect, the characteristics such as small consumption of computing power.

The invention provides a generative image steganography method and system based on a diffusion model and multiple encryption. The method comprises an encryption step and a decryption step. The encryption step comprises the following steps of: A1, converting an input image into a potential code through a VAE encoder; a2, performing forward diffusion on the potential code to obtain a noise vector, and further disturbing the noise vector to obtain a disturbed noise vector; a3, projecting the disturbed noise vector to a new vector space, and generating a reference image through a key and a cue word; step A4, carrying out back diffusion on the noise vector after projection to obtain a steganographic image; and a decryption step: decrypting the steganographic image according to the reference image, the prompt word and the key, and recovering to obtain an input image serving as an original image. According to the method, the information amount of a far hypertext steganography method can be borne, the bottleneck faced by traditional generative steganography when a large data amount is hidden is solved, and the effective load of a steganography channel is remarkably improved.

This invention discloses a generative image steganography method based on a conditional diffusion model, SD-Stego. The algorithm is based on the Guided-Diffusion model, using controllable conditional text as input. During the inverse generation process of the latent diffusion model, an embedding and encoding strategy is designed. DenseNet deep connections and SEBlock channel self-attention mechanisms are introduced to fuse the intermediate generation in the latent space with the message matrix, achieving efficient embedding of steganographic information. Then, a frozen VAE decoder completes the steganographic image generation. In the decoding stage, a multi-layer convolutional network is used to extract high-level features of the generated image layer by layer. Batch normalization and non-linear activation functions are combined to extract embedded information, and the information is gradually mapped through flattening and fully connected layers, finally outputting the decoded message vector. To optimize the steganography effect and generation quality, a joint loss function is designed, including decoding loss and image quality loss, ensuring the stealth of the generated image and the accuracy of message decoding. This invention enables steganography without the need for a carrier image, making it suitable for information security and data steganography. By utilizing denoising generation and text guidance through a conditional diffusion model, the steganography process exhibits advantages such as high robustness, excellent image generation quality, and controllability of generated content. It is more suitable for complex real-world scenarios (such as social network transmission) and has broad application prospects.

The application provides a generative steganography method and system based on a diffusion model and semantic prompt words, relates to the technical field of image steganography, and includes embedding and extraction of secret information. The embedding of secret information is: obtaining secret information to be embedded, converting the secret information to binary and segmenting the binary to obtain a segmented binary sequence; using a pre-constructed semantic prompt word dataset, mapping the binary segments in the sequence into semantic prompt words to form a semantic prompt word sequence, and enhancing the semantic prompt word sequence; inputting the enhanced semantic prompt word sequence into an image generation diffusion model as a condition to guide the model to generate secret images related to the semantics of the prompt words; extracting the latent space representation vector of the secret images, inputting the vector into the image steganography diffusion model after shuffling, and generating a stego image unrelated to the semantics of the prompt words. The application can effectively improve the secret information capacity of a single generated image and improve the overall quality and naturalness of the generated image by the diffusion steganography model.

The invention belongs to the technical field of image steganography, and particularly relates to a project digital management method and system based on big data, and the method comprises the steps: obtaining image data in project information, enabling all pixel points in the image data to form an initial sequence according to a sequence, dividing the initial sequence into a plurality of subsequences with the same length, a first pixel point in a subsequence and other pixel points form a pixel point pair in sequence, secret information is intercepted from a quinary number corresponding to copyright information of an enterprise, only the gray value of a second pixel point forming the pixel point pair is changed through an EMD information hiding algorithm, one piece of secret information is embedded into each pixel point pair, and the information hiding efficiency is improved. And forming a secret-carrying image by using all the subsequences embedded with the secret information, wherein the secret-carrying image is used for realizing copyright protection of image data in project digital management. According to the invention, the copyright protection capability of the image data in the project information is enhanced.

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

The invention discloses an image steganography method based on cavity convolution, which comprises the following steps: selecting a carrier image, and determining secret information; carrying out cross-scale feature fusion on the carrier image and the secret information through an encoder which introduces a multi-stage cavity convolution module, and generating a secret-containing image; using an improved steganalysis discriminator to carry out adversarial training on the secret-containing image, optimizing statistical distribution of the secret-containing image, and obtaining a probability that the secret-containing image is a real image; and decoding the secret-containing image through a decoder to obtain recovered information. According to the method, the high embedding amount and the high coding picture quality can be ensured, meanwhile, the higher noise attack resistance is achieved, and the problem that the robustness based on a coding-decoding network model is poor is effectively solved.

The application provides a vector image steganography method, a true identification method, a device and equipment, and belongs to the technical field of data encryption. The application solves the problem that paper labels are easy to be counterfeited. The vector image steganography method comprises the following steps: obtaining a vector image; performing segmentation and extraction processing on the vector image to obtain a subgraph vector point set; performing stroke search processing on the subgraph vector point set to obtain a stroke point set; and performing modified stroke coordinate processing on the stroke point set to obtain a steganographed vector image. The scheme realizes concealed modification of the graph vector structure without damaging the label design.

The invention discloses a generative image steganography method based on a diffusion model. The method comprises the following steps: 1, designing a mapping function which takes secret information as a drive and is based on probability sampling; 2, hiding secret information in an image generation process of a diffusion model; 3, the receiver reproduces the generation process of the diffusion model by using the shared random seeds; and 4, extracting secret information by using an anti-quantization error extraction mechanism. By constructing the dynamic mapping relation between the discrete secret information value and the continuous probability distribution, the distribution consistency of the secret-carrying image and the normally generated image is strictly maintained, and a pre-training model does not need to be finely adjusted or an additional model does not need to be trained, so that the calculation complexity and cost are remarkably reduced; and the high extraction accuracy of the secret information can be realized from the secret-carrying image in the PNG format.

The invention discloses a face image steganography method based on speckles. The method comprises two steps of image encryption and decryption. The method comprises the following steps of: 1, generating a speckle image from transmission light of a plaintext face image through a scattering medium, modulating the speckle image by using a random phase template, and diffracting the modulated image through Fresnel transformation to generate a ciphertext; and 2, constructing a GAN network, taking the ciphertext as input, taking the plaintext as a label, training the GAN network by using the data set, inputting the ciphertext obtained in the step 1 into the trained GAN network, and outputting a decrypted face image. According to the single image encryption and decryption scheme, the GAN is adopted, the RD serves as a basic module of a generator, a U-shaped structure is formed in combination with jump connection, BN is prevented from being used, feature loss is added into a loss function, and the restoration quality and the detail retention effect of a decrypted image are improved.

The application provides an image steganography method and device for user hierarchical management, equipment and medium. The DWT coefficients of a plaintext image are compressed and blocked by using a compression sensing technology, and then a threshold secret image sharing method is used to dynamically set differentiated threshold values for each image block. The image blocks are steganographed into a carrier image by combining a double-image embedding method, so that a ciphertext image with visual significance is finally generated. Different threshold values are set for different image blocks, so that hierarchical steganography and extraction of the image are realized. When the number of users participating in decryption is less than a set threshold value, no effective information related to the plaintext image can be obtained. When the number of users participating in decryption is greater than or equal to the threshold value, information related to the plaintext image can be obtained, and with the increase of the number of participating users, the reconstruction quality of the plaintext image is improved.

The invention discloses a depth image steganography method and device based on a black box generation model, and the method comprises the steps: firstly generating a carrier image and a fluctuation image through the black box generation model, screening an effective fluctuation image through a mean square error estimation function MSE threshold value, and guaranteeing that the similarity of the screened fluctuation image and the carrier image meets the requirement; constructing an encoder and a decoder, and constraining the encoder through a multi-loss function to generate a steganographic image; through confrontation training of the discriminator and the encoder, the generated steganographic image accords with fluctuation distribution of the fluctuation image generated by the black box generation model, and the anti-detection capability of the steganographic image is improved. According to the method, the steganographic image is disguised as model natural fluctuation by mining the parameter fluctuation rule from the text to the image generation model, so that the detection resistance and generalization ability of the steganographic image are effectively improved.

The invention provides a vector image steganography method, a vector image authentication method, a vector image authentication device and vector image steganography equipment, belongs to the technical field of data encryption, and solves the problem that a paper label is easily counterfeited. The vector image steganography method comprises the following steps: acquiring a vector image; carrying out segmentation and extraction processing on the vector image to obtain a sub-graph vector point set; performing stroke search processing on the sub-graph vector point set to obtain a stroke point set; and carrying out stroke coordinate modification processing on the stroke point set to obtain a steganographic vector image. According to the scheme, under the condition that the label design is not damaged, hidden modification of the graph vector structure is achieved.

This invention discloses an adaptive asymmetric image steganography method and system based on a diffusion model, relating to the fields of artificial intelligence and information security. The invention calculates the corresponding cumulative distribution probability based on the noise to be embedded, then embeds secret information by slightly modifying the cumulative distribution probability. Finally, an inverse cumulative distribution function is used to obtain Gaussian noise with embedded information, maintaining the probability distribution of the embedded Gaussian noise unchanged. Before actual embedding, embedding is simulated in the noise to be embedded, completing simulated embedding +1 and -1 processes to remove pixels that cannot correctly extract information, and calculating the modification amount introduced by embedding +1 and -1 on the noise. Then, the asymmetric steganography embedding cost of each pixel is calculated based on the modification amount, and the secret information is embedded into the cumulative distribution of the Gaussian noise. Finally, the reverse process is completed to generate the steganographic image. This addresses the problems of insufficient anti-detection and imperceptibility in existing steganography methods.

This invention discloses an image steganography method based on generative adversarial networks, comprising: constructing a generator network based on a U-Net encoder-decoder architecture and a discriminator network for adversarial training using large convolutional kernels without using complex network structures and deconvolution; inputting a carrier image into the generator to generate an embedding probability model, simulating ideal embedding using a double-tanh function to obtain a simulated steganographic image, and training the discriminator using a cross-entropy loss function; designing a loss function for the generator and training the generator based on the discriminator's cross-entropy loss, embedding capacity, image visual similarity, and high-pass filtering error; repeating the above steps while iterating the generator and discriminator multiple times until the entire training is completed; and generating a steganographic image using the trained network and the STC encoder.

The application discloses a reversible image steganography method based on block-level discrete capacity upper limit, and relates to the technical field of image steganography and reversible information hiding. The method comprises the following steps: performing reversible multi-scale transformation on a carrier image, establishing a block index, generating a block-level discrete capacity upper limit mapping, encoding hidden content into a bit stream and performing upper limit division and scheduling, dividing a straight-through branch and a bearing branch in a reversible coupling unit, generating a mapping parameter from a multi-scale conditional feature and the straight-through branch, writing a load into the bearing branch under block-level gating, performing inverse transformation after merging to obtain a steganography image and generating navigation auxiliary information, performing inverse transformation and layer-by-layer inversion during extraction, and combining the capacity upper limit mapping and the auxiliary information to read and recombine the load block by block. The application realizes content-adaptive discrete quota control, improves the hidden capacity and extraction reliability while ensuring strict reversibility and visual quality, has low auxiliary information overhead, and is suitable for reversible steganography scenes with high fidelity and high capacity.

This invention discloses a speckle-based face image steganography method, which includes two steps: image encryption and decryption. First, the transmitted light from a plaintext face image is passed through a scattering medium to generate a speckle image. This speckle image is modulated using a random phase template, and then diffracted using a Fresnel transform to generate ciphertext. Second, a GAN network is constructed, using the ciphertext as input and the plaintext as a label. The GAN network is trained using a dataset, and the ciphertext obtained in step one is input into the trained GAN network, outputting the decrypted face image. This invention's single-image encryption / decryption scheme employs a GAN network, using RD as the basic generator module and combining it with skip connections to form a U-shaped structure, avoiding the use of BN. Furthermore, feature loss is added to the loss function, improving the reconstruction quality and detail preservation of the decrypted image.