428results about "Image watermarking" patented technology

The invention discloses a generative invisible watermarking method based on a stable diffusion model, which comprises the following steps of: processing a text prompt / image prompt to obtain a latent variable, guiding to generate an image by using the latent variable, converting the image into a low-dimensional potential representation through a variational auto-encoder, generating potential noise according to the low-dimensional potential representation, and generating a stable diffusion model according to the potential noise. Performing multiple iterations by combining the potential noise and the low-dimensional potential representation to generate a noise-containing image; a watermark decoder is utilized to finely adjust the decoder, so that the finely-adjusted weight watermark is embedded into the generated image; and meanwhile, when the stable diffusion model generates an image, compressing the image into potential noise by using a variational auto-encoder, then performing wavelet transform on the obtained potential noise to obtain a noise watermark, mapping noise watermark information into an annular pattern watermark, embedding the annular pattern watermark into a noise low-frequency sub-band, and performing image generation by using the watermark-containing noise to obtain a watermark-containing image. According to the method, the watermark is embedded in the image generation process, and the watermark information in the generated image can be extracted.

The invention provides an image watermark generation method, an image watermark generation device and a computer storage medium. The image watermark generation method comprises the following steps: acquiring original potential features of an original image; watermarking information with the same size as the original potential features is fused with the original potential features, and fused potential features are obtained; acquiring a pixel-level visual sensitivity mask parameter of the original image; adjusting the watermark intensity of the fused potential feature by using the pixel-level visual sensitivity mask parameter to obtain an adjusted potential feature; generating an image watermark according to the adjusted potential features; and generating a watermark image according to the image watermark and the original image. According to the image watermark generation method, the watermark embedding strength is adaptively adjusted by using the pixel-level visual sensitivity mask parameters, so that global collaborative adaptation of watermark energy distribution and image visual characteristics is realized, and the robustness of the watermark to composite attacks is remarkably improved.

The invention relates to a method for generating an image watermark based on potential space multi-scale feature modulation. The method mainly comprises a watermark residual embedding and extracting method based on potential space multi-scale feature modulation, a watermark intensity regulation and control strategy based on time sequence gating and a robust watermark end-to-end training framework based on potential space. According to the method, watermark information is fused by using a multi-scale structure in a potential space of a diffusion model, meanwhile, a time sequence gating strategy dynamically determines the embedding time step of a watermark by calculating the convergence degree of a potential vector, and the image quality and the watermark robustness are balanced. Sigmoid weighted noise prediction loss is introduced in the model training process, and denoising control over low time steps is enhanced. The method can be widely applied to scenes such as image generation, copyright identification and content traceability, and has good practicability and popularization prospects.

Image fingerprints (embeddings) are generated by an image fingerprinting model and indexed with an approximate nearest neighbors (ANN) model trained to identify the most similar fingerprint based on a subject embedding. For image matching, a score is provided that indicates a similarity between the input embedding and the most similar identified embedding, which allows for matching even when an image has been distorted, rotated, cropped, or otherwise modified. For image classification, the embeddings in the index are clustered and the clusters are labeled. Users can provide just a few images to add to the index as a labeled cluster. The ANN model returns a score and label of the most similar identified embedding for labeling the subject image if the score exceeds a threshold. As improvements are made to the image fingerprinting model, a converter model is trained to convert the original embeddings to be compatible with the new embeddings.

The invention discloses a deep counterfeit face image source tracing evidence obtaining method and system based on a diffusion model, and relates to the technical field of deep counterfeit face image evidence obtaining. The method comprises the following steps: constructing a robust watermark framework based on a diffusion model, wherein the framework comprises a diffusion model encoder, a watermark decoder and a freeze parameter auto-encoder; in a training stage, a face image and a watermark are constructed as conditions, a diffusion model encoder is guided to denoise, and a corresponding watermark image is predicted; the trained robust watermark framework based on the diffusion model is used for testing, a specific depth forging model is added in the sampling stage for guiding, and a more robust watermark image is generated through iterative denoising; in the evidence obtaining stage, the watermark in the forged image to be subjected to evidence obtaining is obtained through the watermark decoder. The method can effectively cope with different counterfeiting technologies, provides a more robust and reliable deep counterfeiting traceability evidence obtaining scheme, and has strong adaptability and wide application prospects.

The invention discloses a diffusion model robust watermarking method based on affine coupling flow accurate coding. The method comprises the following steps: S1, training a watermark encoder and a watermark decoder; s2, freezing the trained watermark encoder and the diffusion model to perform diffusion iteration on the initial hidden variable to obtain a watermark-containing image, introducing an attack layer to finely adjust the pre-trained watermark decoder to generate the watermark-containing image, performing reverse diffusion of the diffusion model to form the initial hidden variable, and extracting watermark information by the pre-trained watermark decoder; and S3, uniformly and integrally deploying the trained watermark encoder and the diffusion model, and extracting the watermark in the user generated image to verify the identity. According to the diffusion model robust watermarking method based on affine coupling flow accurate coding, the problems that the robustness of a reconstruction watermark removing technology is insufficient, and the presentation of watermark information in an image generated by a user is single are solved.

The invention discloses a watermark imperceptible embedding and recovering method based on a deep learning network structure. Firstly, a mask guide watermark embedding scheme is designed, a mask generation module is constructed by using a residual dense feature extraction module and an attention mask generation module, and a watermark is adaptively guided to be embedded into an image texture rich area so as to improve the invisibility of the watermark. And secondly, constructing a watermark decoding network based on comparative learning, and by comparing a loss function, taking the decoding features of the same watermark image under different noise conditions as positive samples and taking the decoding features of different watermark images as negative samples so as to enhance the consistency of the decoding features, thereby improving the robustness of the watermark. The deep learning network structure can improve the robustness of the watermark in a real screen shooting scene, and has a huge application value in copyright protection and traceability tracking.

The invention belongs to the field of image processing, and relates to a method for embedding a robust watermark in a diffusion model generated image, which comprises the following steps of: acquiring cue words, initial Gaussian noise and watermark information, and inputting the cue words, the initial Gaussian noise and the watermark information into a trained diffusion model based on watermark embedding to obtain a watermark-embedded image; the training process of the diffusion model comprises the following steps: acquiring cue words, initial Gaussian noise and watermark information, and inputting the cue words, the initial Gaussian noise and the watermark information into an encoder to obtain potential vectors; inputting the potential vector into a self-attention module to obtain an embedded position vector; embedding the watermark information into the potential vector according to the embedding position vector; inputting the potential vector embedded with the watermark information into a decoder to obtain an image embedded with the watermark; extracting watermark information from the image embedded with the watermark; updating parameters of a diffusion model according to the image embedded with the watermark and the extracted watermark information until a trained diffusion model is obtained; according to the method, the embedding position is selected by combining the potential of the diffusion model and the accuracy of the self-attention mechanism, so that efficient watermark embedding and extraction are realized.

The invention discloses a deep counterfeiting active detection method based on block chain and robust watermark enhancement, and relates to the technical field of image processing, and the method is characterized in that the method comprises the following steps: S1, training a watermark embedding extraction network; s2, embedding a 100-bit random binary bit watermark into the original face image through a watermark embedding model, and storing watermark information to a block chain; s3, training a deep forgery detector, and carrying out image enhancement on the watermark image; and S4, performing deep counterfeiting on the enhanced watermark image, and extracting the watermark from the deep counterfeiting image. The technical problem to be solved by the invention is to provide a deep counterfeiting active detection method based on block chain and robust watermark enhancement, and an efficient and reliable solution is provided for deep counterfeiting detection and traceability by embedding and extracting watermark information and combining a block chain technology.

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.

A computer-implemented method for watermarking images includes providing a secret key network (SKN) that is adapted to output a standard multivariate normal (SMVN) distribution for a given input image distribution, applying an input image to the SKN, generating a secret key signature (SKS) as a real vector, and embedding a watermark in the input image by using an adversarial attack to modify the input image in a manner that aligns the SKN's output with the SKS. A computer-implemented method for detecting a watermark in an image is also provided.

The invention relates to a deep forgery active defense method based on watermarking, which belongs to the technical field of information security, firstly provides a semantic self-adaptive watermark embedding method based on Transform, and dynamically distributes watermark weights according to the semantic importance of a human face area by using a cross attention mechanism so as to enhance the defense effect on deep forgery attack; secondly, a separable decoder watermark architecture is constructed, the separable decoder watermark architecture comprises a robust decoder and a semi-robust decoder, a random image processing module (RIPM) is introduced during training, the separable decoder is trained by simulating images of conventional distortion and deep forgery attack, and stable traceability of user identity information and recognition of deep forgery behaviors are achieved; meanwhile, local watermark concentration difference caused by deep counterfeiting is analyzed, a thermodynamic diagram of a counterfeiting region is generated in combination with supervised learning, and positioning of the deep counterfeiting region is realized.

The invention relates to an image steganography method. The method comprises the following steps: carrying out content analysis on an original image serving as a carrier; generating a self-adaptive embedding strategy according to an analysis result, and encoding the embedding strategy into a compact binary format; embedding to-be-embedded data into different areas and channels of the original image in a layered manner according to an embedding strategy to obtain an embedded image; the embedded data is extracted from the embedded image by decoding the embedding policy and mapping reconstruction. According to the method, a region self-adaptive embedding strategy is formulated, the embedding capacity is maximized on the premise of keeping the visual quality of the original image, and the hidden data detection risk is reduced.

The invention discloses an image watermarking method based on a space channel interactive attention mechanism. The image watermarking method mainly comprises the following steps: acquiring an original image and constructing an image watermarking network based on the space channel attention mechanism; inputting the original image and the watermark into a watermark-containing image generation framework to obtain a watermark-containing image; inputting the image with the watermark into an image processing noise layer to obtain a noise image; inputting the noise image into a watermark extraction framework to obtain an extracted watermark; and constraining the training of the whole image watermark network based on the space channel attention mechanism by using the total loss. The image watermark network based on the space channel attention mechanism is used for copyright protection, and high-capacity embedding of watermarks, high-quality generation of images with watermarks and high-robustness extraction of the watermarks are achieved. According to the method, a depth feature map of the watermark is generated through watermark dimension extension, remodeling and diffusion by using a multi-layer sensor and the like. Meanwhile, the feature retention loss can promote the optimization of the total loss on the network training.

The embodiment of the invention provides a method for determining a generative model. The method includes embedding a white box watermark and a black box watermark in a generative model. Black box watermarks are first embedded into probability density functions of data abstraction in different layers of the generative model. The method also includes embedding the white box watermarks into different layers for generative model output after the embedding of the black box watermarks is completed. Model data is generated by the generative model based on predetermined trigger data. The predetermined trigger data comprises a predetermined trigger text or a predetermined trigger image. An identity associated with the generative model is determined based on the model data. By using the method, white box and black box attacks can be resisted by embedding two complementary and independent watermarks, so that double-layer protection is provided for the generative model.

The invention discloses a deep watermarking method for immune geometric distortion, and the method comprises the steps: firstly constructing a watermark model in combination with a Swinin-Transform and a deformable convolutional network (DCN), and enabling the watermark model to comprise a watermark encoder and a watermark decoder; simulating styles of different types of non-geometric distortion by using a style migration network to form a distortion style combined noise layer; and applying the distortion style combined noise layer to the training of the constructed watermark model, enhancing the multiple robustness of the watermark model to various non-geometric distortions, and improving the generalization ability of the robustness. According to the method, the image copyright protection and image leakage prevention functions can be realized while the watermark embedding is ensured not to influence the image quality, so that the intellectual property and security protection of the image can be enhanced.

A system for adding copyright protection to implicit 3D models is provided. The system includes a first MLP module, a second MLP module, a color feature encoder, a message feature encoder, and a feature fusion module. The first MLP module outputs a geometry parameter according to a 3D coordinate parameter obtained from a 3D model source. The second MLP module outputs a base-colors parameter according to a viewing-directions parameter obtained from the 3D model source and outcomes of the first MLP module. The color feature encoder concatenates the geometry parameter, the viewing-directions parameter, and the base-colors parameter to obtain a spatial descriptor and transforms the spatial descriptor to a high-dimensional color feature field. The message feature encoder maps messages to higher dimensions so as to obtain a message feature field. The feature fusion module generates a watermarked color representation and embed the watermarked color representation into the 3D model source.

According to the invisible watermark embedding method based on the wavelet domain statistical characteristics, the high-frequency sub-band in the wavelet domain is selected as the embedding area, and the robustness of the watermark to conventional attacks such as compression and noise is improved while invisibility is guaranteed. Based on a self-adaptive embedding weight mechanism of a covariance matrix eigenvalue, the embedding strength of different texture regions can be dynamically adjusted, the robustness is enhanced by a smooth region, and distortion is suppressed by an edge region. Nonlinear disturbance is generated by adopting a deep neural network and chaotic encryption preprocessing is combined, so that the predictability of a linear statistical rule is broken, and the method has a relatively good application prospect.

The invention discloses a robust reversible watermarking method based on BFMs and adaptive normalization. The method comprises the steps of watermark embedding and watermark extraction. The watermark embedding specifically comprises the steps of generating watermark sequence information, performing integer wavelet transform on a carrier image, extracting a low-frequency region, and calculating BFMs of the low-frequency region; a proper watermark pair is selected through a private key, self-adaptive normalization is carried out according to the watermark pair, and the watermark is embedded in the normalized amplitude; reconstructing an image based on the amplitude of the embedded watermark; constructing a final reversible watermark image based on the reconstructed image; watermark extraction: judging whether the image is attacked or not; for an unattacked image, carrying out watermark extraction based on the auxiliary information; for an attacked image, watermark extraction is carried out by calculating the BFMs of the attacked image. The method has noise resistance and also has relatively good image reconstruction capability.

A system and method for producing color calibrated videos using a calibration slate are provided. A video of a subject is recorded with a calibration slate appearing in at least one frame of the video. The calibration slate includes printed colors that can be compared against standard colors associated with the slate to color calibrate the video. Once at least one frame of the video has been color calibrated, calibrated colors of the calibrated frame may then be utilized for color calibration of additional frames of the video.

The invention discloses a forged face image generation method and recognition method based on a diffusion model, and the method comprises the steps: collecting various watermark signatures used for a forged face image and a plurality of vanilla images, training an encoder and a decoder of a HiDDeN model, and employing the trained decoder as a watermark extractor to carry out the detection of a watermark in the forged face image; a plurality of vanilla images are collected to form a data set, an original watermark signature m is distributed to each training sample in the data set so as to train an LDM network model, a watermark extractor is adopted to carry out watermark extraction on output images of the LDM network model, and a watermark signature # imgabs0 # is obtained; and constructing a total loss function by using the watermark signature # imgabs1 #, the corresponding original watermark signature m and the image perception loss, and finally acting the total loss function on a back propagation process of a decoder of the LDM network model, so that the LDM network model learns the capability of generating the watermark image.

The invention provides an anti-screen-shooting robust text image watermarking method fusing edge attention gating and multi-scale cavity convolution, which comprises the following steps: step 1, designing a message processor for preprocessing a secret message, adding an expansion network ExpandNet and a channel attention SENet in the message processor, and carrying out edge attention gating and multi-scale cavity convolution on the expansion network ExpandNet and the channel attention SENet; inputting a secret message in a binary form and outputting a message feature map; step 2, constructing a text watermark framework based on edge attention gating and multi-scale cavity convolution fusion; adding a functional module in an encoder of the end-to-end watermark framework; 3, designing a noise layer, and performing simulation attack on the model in the watermark model training process; and step 4, designing an optimization mode of the loss function. According to the method, the robustness and imperceptibility of the watermark are remarkably improved.

The invention relates to an image watermark generation method and device based on a thinking chain strategy and computer equipment. The method is based on a visual perception visual large model, after knowledge enhancement is carried out on the visual large model through expert knowledge, the visual large model firstly analyzes the content of a picture, then a watermark scheme is given, then a segmentation model and an edge optimization module are called, and finally a watermark is rendered to the image. According to the method, the balance between the visual effect of the artwork and copyright protection is fully considered in the watermark generation process, and the technical path from art aesthetics, image processing to watermark embedding ensures that the generated watermark not only has high aesthetics, but also can effectively ensure the visibility and non-tampering property of the watermark.

The invention discloses a vision enhancement robust digital dark watermarking method based on deep learning, a storage medium and equipment, and belongs to the technical field of digital image processing and information security. According to the method, a visual enhancement module comprising a self-adaptive watermark region adjustment module and a frequency enhancement module is constructed, and a multi-region local loss training mechanism and a screen shooting simulation module are combined, so that the visual quality of a watermark image is improved, and meanwhile, the robustness of the watermark image for resisting physical attacks such as screen shooting is enhanced. The specific processing flow comprises the following steps: acquiring an original image and watermark information; embedding the watermark information into the image by using an encoder, wherein an embedded region is optimized by a self-adaptive region guide matrix generated based on the image edge and gray information; in the model training process, a noise layer simulating screen shooting physical distortion is introduced, and the area weight is dynamically adjusted according to residual error distribution in the later stage of training so as to focus and optimize the image quality; watermark information is extracted from an image which may suffer from an attack by using a decoder.

The invention discloses a watermark embedding method, a watermark extraction method, equipment, a readable storage medium and a product, and relates to the technical field of image processing, and the method comprises the steps: obtaining an original image to be subjected to watermark embedding and watermark information to be embedded, and inputting the original image and the watermark information to a pre-trained watermark embedding model; performing feature extraction on the original image through a feature extraction network to obtain a multi-domain fusion feature; a watermark embedding mode is determined through an adaptive attention network according to the multi-domain fusion features, and the watermark embedding mode at least comprises a watermark embedding position; watermark information is embedded in the multi-domain fusion features in a residual error mode through a watermark embedding network based on a watermark embedding mode, and embedded features are obtained; and through an image reconstruction network, the embedded features are converted to an airspace to obtain a watermark image after watermark embedding. According to the method, the watermark robustness is ensured, and meanwhile, the influence on the visual quality of the image is reduced.

The invention discloses a generative model redrawing attack-oriented image robust watermark traceability method and system and a computer readable storage medium, and belongs to the field of digital information security and artificial intelligence content governance. The method comprises the following steps: in a watermark embedding stage, performing error correction coding and digital signature processing on traceability information containing identity information and a timestamp to generate a load to be embedded, and embedding the load to be embedded and a synchronization template for geometric synchronization into a host image in a function separation manner by using a deep neural network; in the training stage, combined optimization is carried out on the watermark embedding and extracting process by introducing generative model redrawing and microsimulation of image distortion attack, so that the robustness of the watermark under a complex attack condition is improved; in the extraction and verification stage, under the condition that an original image is not needed, geometric synchronous correction and blind extraction of a watermark load are carried out on an image to be analyzed, and authenticity confirmation of traceability information is completed through error correction decoding and digital signature verification. By adopting the technical scheme of the invention, the problems of insufficient traceability information robustness, difficult source confirmation and incomplete evidence chain in a generative model redrawing scene in the prior art are solved, and verifiable traceability and credible evidence generation of the image source information are realized.

The invention provides a high-fidelity anti-compression image watermarking method and system based on spectrum-airspace decoupling, and belongs to the field of information security. Firstly, the watermark information is mapped and remodeled; a watermark encoder based on multi-granularity spectrum-spatial domain feature decoupling is constructed, discrete cosine transform is introduced to filter out high-frequency components, Haar wavelet transform is adopted to realize lossless downsampling, a fast Fourier transform dynamic filter is combined to capture global semantic features, and local texture details are combined through multi-scale spatial domain volume accumulation; designing a physical perception and visual self-adaptive dual embedding strategy, and anchoring watermark energy to an anti-compression brightness channel; constructing an anti-attack layer containing differentiable JPEG compression simulation and mixed noise simulation, and participating in network training; and constructing a decoder and designing a loss function to carry out network optimization. According to the method, the robustness of the watermark under strong compression and complex black box attacks is improved, and extremely high visual imperceptibility is realized through physical and visual constraints.

The invention discloses a copyright protection method for a 3DGS three-dimensional scene and objects in the scene, and belongs to the technical field of three-dimensional reconstruction. Target objects needing to be protected are automatically extracted from a multi-view image data set through an image sequence segmentation model; combining the original image with the target object image to construct a new data set for watermark network training, and carrying out network training; performing high-quality scene reconstruction on the multi-view image by adopting a 3DGS method; a hidden information loss function is introduced to finely adjust scene color information and a decoder of a watermark network, and copyright watermark embedding of the whole scene and a target object is achieved; the frequency domain consistency loss and the image difference loss are combined and optimized, and the quality of the reconstruction model after the watermark is embedded is ensured. According to the method, copyright protection of a three-dimensional scene level can be realized, fine-grained protection can be carried out on specific objects in the scene, and the method has relatively high practicability and robustness and is suitable for application scenes such as digital asset management and virtual reality content protection.

The invention discloses a DCT (Discrete Cosine Transform) frequency domain watermark embedding optimization method and system based on a potential space, and the method comprises the steps: firstly coding an original image into the potential space, segmenting the original image into small blocks, embedding a single-bit watermark into the frequency domain of each small block through weighted voting, then carrying out the inverse transformation to a spatial domain, merging, and forming a potential representation with a watermark; decoding through a stable diffusion model to generate an image with a watermark; iteratively optimizing the potential representation through a multi-objective loss function until the loss is minimum; and finally, decoding the optimized representation again, linearly mixing with the original image, adjusting a mixing coefficient through binary search, and outputting a final image on the premise of meeting preset image quality. According to the method, a multi-dimensional robustness optimization strategy is combined, the stability and the non-erasable property of the watermark under the attack of the generative model are systematically improved, and the core requirements of AI copyright protection and content traceability are met.

The invention discloses an anti-screen-shooting robust watermarking method based on a generative adversarial network and multiple tokens, and belongs to the technical field of digital watermarking. According to the method, firstly, watermark information is converted into image features through an encoder, the features are embedded into an original image, and a watermark image which is visually indistinguishable from the original image is generated; then inputting the watermark image and the original image into a discrimination network for feature analysis; in the training process, processing of simulating screen shooting distortion is applied to the watermark image; the watermark information is then recovered from the noise image by a decoder. According to the method, the coding mode of the watermark information is improved, and the robustness of the watermark in a real screen shooting environment is remarkably improved while the visual invisibility is ensured; especially, when the interference such as geometric deformation, moire and color distortion generated by screen shooting is handled, the integrity of the watermark data can be more reliably maintained, and high decoding accuracy is maintained.