7 results about "Image steganography" patented technology

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.

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.

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 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.

The application discloses an image steganography method based on a residual improved MWCNN network and belongs to the technical field of covert communication. The method improves the image steganography scheme based on the MWCNN network, changes the network downsampling from the traditional pooling operation to the wavelet transform, introduces the residual network and the wavelet component skip-layer fusion into the image steganography network, and can obtain a better hiding effect in the aspect of color image steganography. Meanwhile, the method can improve the extraction image visual quality. The steganography scheme can hide large-capacity image information while guaranteeing the quality of the carrier image and the extraction image accuracy.

This invention discloses an error-resistant robust image steganography method based on low-frequency embedding without background noise. The method includes: preprocessing and hybrid encoding the original secret image to generate a final secret embedding stream; performing a 5 / 3 integer wavelet transform on the carrier image to decompose the frequency domain, extracting the first-level low-frequency sub-band, calculating texture activity in blocks, and generating an embedding mask; setting the quantization step size and jitter bias based on quantization index modulation, adjusting the block mean based on the bits to be embedded, and superimposing the offset to complete the embedding; performing integer wavelet transform and block statistics on the received image, obtaining the initial bit stream using the minimum distance method of quantization index modulation, and reconstructing the output secret image through deinterleaving, error correction decoding, inverse whitening, and inverse scrambling. This invention solves the problems of poor security and weak resistance to burst errors in traditional steganography, achieving a balance between robustness and concealment, and improving the reliability of covert communication in complex channels.

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