294 results about "Watermark method" 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 discloses a method for embedding a text watermark during Logs generation based on a large language model. The method comprises the following steps of: analyzing a generated text tl-1 of a large model through dependency syntax analysis, constructing a dependency syntax tree of the generated text tl-1, sorting according to scores of a dependency syntax relationship, and selecting and adding biased Logs; a candidate word list is generated, probability distribution corresponding to each dependency type is calculated, the selection probability of the candidate word list is adjusted, vocabularies are sampled and generated at key positions from the candidate word list according to a set rule, and a watermark embedded text is obtained; the candidate word list comprises a first list and a second list, and the selection probability of the first list is higher than that of the second list; and extracting a to-be-detected fragment in the watermark-embedded text, detecting the watermark of the text, and outputting a detection result. According to the method, the problem that the anti-attack capability of adding watermarks to the generated text is insufficient is solved, and meanwhile, the problems of text quality and large long text calculation amount of a current watermarking method in the Logits generation period based on a large model are solved.

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.

The invention relates to the field of watermarking algorithms, and provides a watermarking method and system for generating a text by a large language model. The method comprises the steps that a prompt text and a generated text sequence are input into a generation model, and an original logic score of a next mark position is generated through probability distribution calculation; performing semantic feature extraction on the generated text sequence by using an embedding model to obtain semantic embedding representation; converting the semantic embedding representation into a watermark logic score based on a pre-trained watermark model; and performing weighted combination on the original logic score and the watermark logic score to generate a final logic score, and outputting a watermark mark based on the final logic score. According to the method, the high robustness of the watermark is improved, and the safety of the watermark is also improved.

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 robust video watermarking method and system based on frame domain attack training. According to the invention, a model training framework composed of an encoder module, a noise attack module, a generative adversarial module and a decoder module is constructed; and taking minimization of the total loss function as a target, training the model training framework by using training data, and introducing a plurality of noise attacks and watermark removal attacks in the training process to improve the robustness of the model. In practical application, the trained encoder module is utilized to add watermark information to a target video, and the trained decoder module is utilized to extract the watermark information from the watermark video. According to the method, the noise attack and the watermark removal attack are introduced in the training process, so that the stability and the extraction precision of the watermark in a complex attack environment can be improved.

The invention relates to the technical field of model watermarking, and discloses a multi-modal model watermarking method based on synonym replacement, which comprises the following steps of: inputting a given image and a prompt into a multi-modal model to generate a text sequence for describing the image or a text sequence for answering a question proposed based on the image; watermark information can be detected in the text sequence; the method comprises the following steps of: mapping a secret image into a hash value as a private key by using a perceptual hash algorithm; dividing the vocabulary by using a private key and changing an output sample to embed watermark information; and checking whether the watermark exists or not by counting the number of words in a green list in a text sequence output by the multi-modal model. According to the method, the watermark can be embedded into the multi-modal model only by changing the text output sample, retraining is not needed, and detection can be carried out without accessing the model, so that the method is suitable for all mainstream multi-modal model structures, and strong generalization is provided.

The invention relates to a watermarking method based on mutual information screening and IGWO optimization, and the method comprises the following steps: 1, carrying out the attribute screening through a mutual information method, obtaining an attribute set which is highly related to the classification accuracy, determining an unimportant attribute set, and forming a complete attribute set after the mutual information screening; step 2, based on the attribute set screened by the mutual information attributes in the step 1, carrying out watermark embedding operation for an unimportant attribute set to obtain a watermark data set containing a watermark; and step 3, based on the watermark data set generated in the step 2, watermark data are classified through a decision tree model, and the watermark embedding strength and position are optimized by applying an IGWO algorithm. The method can effectively solve the problem of traceability responsibility of matched power grid service data.

The invention relates to the technical field of digital image processing, in particular to a digital image mixed watermarking method based on time domain and frequency domain synchronization. According to the method, a time domain and frequency domain synchronous watermark embedding algorithm and a multi-stage watermark extraction algorithm are innovatively combined, so that the concealment and robustness of the watermark are remarkably improved, and the problems of digital image copyright protection in the face of printing and scanning attacks and network attacks are effectively solved; the method ensures the security and reliability of digital image copyright protection, provides a more advanced and effective technical means for digital image copyright protection, has the advantages that a time domain and frequency domain synchronous watermark embedding algorithm enables watermarks to be fused into image details and is high in concealment, the watermarks are effectively protected in the face of various attacks, and the security and reliability of digital image copyright protection are improved. The multi-stage watermark extraction algorithm ensures accurate extraction when the image quality is reduced, and the watermark performance is integrally 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 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 invention discloses a wavelet domain audio zero watermark algorithm based on dual-tree complex wavelet transform (DTCWT), variational mode decomposition (VMD) and discrete cosine transform (DCT). Firstly, encryption protection is carried out on a watermark image, then framing is carried out on an original audio signal, dual-tree complex wavelet transform (DTCWT) and variational mode decomposition (VMD) are carried out on each frame, a first IMF is taken to carry out DCT, and a feature vector is obtained. And finally, carrying out XOR operation on the polarity feature vector and the scrambled watermark to obtain a zero watermark key. Experiments show that the method can effectively resist conventional attacks, and especially has good robustness to noise attacks.

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.

The invention relates to a robust image watermarking method, system and terminal based on a conditional diffusion model, and relates to the field of robust image watermarking technology, and the method comprises the steps: carrying out the training step in a circulating manner according to the training step information, and the training step comprises the steps: obtaining the training original image information and the training watermark information; determining training condition noise image information according to the training original image information; the training condition noise image information, the training original image information and the training watermark information serve as input to control a training encoder to generate coded image information; taking the coded image information as input to control an attack layer to generate attack image information; the attack image information serves as input to control a training decoder to generate training decoding watermark information; and calculating loss function information according to the training original image information, the encoding image information, the training watermark information and the training decoding watermark information, and updating encoder parameter information and decoder parameter information according to a gradient descent method. The method has the effect of improving the watermark performance.

The invention discloses a rapid watermarking method based on a generative adversarial network, and belongs to the technical field of digital watermarking. According to the invention, based on a rapid generation framework of a consistent watermark, the problem that the existing deep learning watermark method needs a large amount of computing resources is solved. A traditional method needs to repeatedly call a depth model to generate a watermark for each image or video, and the calculation cost is high. According to the method, the watermark can be embedded into any image or video only by generating the watermark once, and a depth model does not need to be repeatedly called. And the watermarking effect can be comparable with that of the existing method. The method is suitable for scenes requiring efficient watermark embedding, and has a wide 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.

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 provides a remote sensing image robust watermarking method, device and equipment, and relates to the technical field of computers, and the method comprises the steps: carrying out the feature extraction of an original image, mapping the extracted image features to a potential feature space through an adapter network, and obtaining the corresponding potential features, and fusing the potential features and watermark information, inputting the fused potential features and watermark information into a generator network, generating a watermark image in combination with a U-shaped structure and a semi-wavelet attention mechanism, performing random distortion simulation on the watermark image through signal processing and geometric transformation, and analyzing distortion features of a distorted image to obtain watermark information. According to the method, an adapter structure is introduced in front of the generator, and the convolution optimization generator is used to pay more attention to effective features, so that the network training cost can be effectively reduced; a semi-wavelet attention module is introduced into the generator, and spatial domain and frequency domain features of the image are fully combined, so that the influence of the watermark on the image quality is reduced, and the robustness of the watermark to complex attacks is improved.

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 an AIGC image watermarking method and system based on diffusion model generation path deflection. The method comprises the following steps: a server distributes a group of private key vectors for each user as a unique identity certificate; generating initial noise through an initial transformation function by combining a random salt value constructed by a user key and image generation time; in the first several steps of diffusion generation, generating path deflection according to key guidance, and embedding watermark semantics into image content in an implicit manner; after completion, publicly recording and generating a timestamp; in the verification stage, estimation noise is recovered through a reverse diffusion process and compared with reference noise reconstructed by a secret key and a timestamp, the mean square error of initialization deviation is calculated, and verification is passed if the mean square error is lower than a set threshold value. According to the AIGC image watermarking method and system based on the diffusion model generation path deflection, robust implantation and high-credibility verification of a copyright watermark can be realized, and the method and system are suitable for copyright confirmation and tracking scenes of an AIGC image generation platform.

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 relates to an active network defense method and system based on watermark and moving target fusion, and the method comprises the steps: obtaining the input and output data of a communication system, and constructing a system model and a holosymmetric polytope observer model according to the input and output data; constructing an attack detector based on the system model and a holosymmetric polytope observer model; and based on the attack detector, the system model and the holosymmetric polytope observer model, watermarking input and output data through a pseudo-random number generation method and a preset target defense function. Through the watermark and moving target removal technology, the problem that the system performance is influenced by an existing watermark method is solved; and meanwhile, false data attacks, denial of service attacks, replay attacks and the like can be detected. In addition, the method is suitable for application scenes needing active attack detection, such as an intelligent connected vehicle system and an intelligent power grid system, and has very high application value.

The invention discloses a video watermarking method based on a reversible neural network, and the method constructs a watermark embedding extraction network which comprises a watermark embedding module and a watermark extraction module which share network parameters, and a distortion module which is used for simulating an HEVC compression effect. Distortion layers in the watermark embedding module, the watermark extraction module and the distortion module are all based on a reversible neural network; pre-training the distortion layer based on the pre-training set, fixing network parameters of the distortion layer after the pre-training of the distortion layer is completed, training the watermark embedding extraction network based on the training set, and obtaining a trained watermark embedding extraction network after the training of the watermark embedding extraction network is completed; during testing, a test set is input into the watermark embedding module to obtain a watermark video, HEVC compression is performed on the watermark video to obtain an HEVC compressed video, and the HEVC compressed video is input into the watermark extraction module to obtain extraction end watermark information; the method has the advantages of being large in watermark embeddable capacity, good in watermark robustness and high in watermark extraction accuracy.

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.

The invention discloses a remote sensing image zero-watermarking method based on depth features, relates to the field of digital watermarking, and aims to solve the problem of insufficient robustness during active feature design. The remote sensing image zero-watermarking method comprises the following steps: reading a remote sensing image, converting the remote sensing image into a gray scale, and normalizing the gray scale image; detecting feature points of the normalized image by using an SIFT algorithm, screening out feature points in a certain intensity interval, and constructing a Delaunay triangulation network according to a rule; calculating inscribed circles of triangles in the triangulation network one by one, constructing an inscribed circle mask set Masks, multiplying the mask set by the remote sensing image, and performing scale normalization processing on a result in order to facilitate feature extraction of the neural network; and inputting the feature domain into a trained U-Net neural network, and extracting robust features in the feature domain. According to the method, on the basis of good global stability and empty circle characteristics of the Delaunay triangulation network, each feature domain of the image exists independently, and good uniqueness of the watermark algorithm is guaranteed.

The invention discloses a high-fidelity robust watermarking method based on deep reinforcement learning. The method is composed of a reinforcement learning module, a watermark encoding and decoding module and a watermark synchronization module in sequence. The reinforcement learning module automatically selects an optimal watermark embedding position in a local area of the image through an adaptive decision-making mechanism of a deep Q network to realize dynamic optimization of an embedding strategy; the watermark encoding and decoding module adopts a convolutional block attention mechanism (CBAM) and a multi-scale feature fusion structure, and realizes high-fidelity embedding and accurate extraction of watermark information while maintaining the visual quality of the image; the watermark synchronization module is based on a U2Net model structure, and completes spatial alignment of a watermark area through mask prediction and affine correction, thereby effectively solving positioning deviation caused by geometric attacks such as rotation, zooming, cutting and the like. The three components work cooperatively, and high watermark extraction precision can be kept under various compression, noise and geometric disturbance conditions. The method has the advantages of imperceptible embedding, high extraction stability, independent training and combined deployment of modules and the like, and can be widely applied to the fields of digital image copyright protection, AIGC (Artificial Intelligence Generation Content) traceability, multimedia authenticity verification and the like.

To discriminate a generation source of text data even after a digital watermark key has leaked.SOLUTION: In a double digital watermark system 1, a digital watermark embedding device classifies, by a first key and a hash function, a plurality of tokens generated next from a first token into a first group or a second group; receives tokens of the first group; classifies, by a second key and the hash function, a plurality of tokens generated next from a second token of a token sequence into a third group or a fourth group; and increases probabilities of tokens of the first and third groups. A digital watermark detector calculates, by the first token and the first key, the number of tokens of the first group; calculates, by the second key, the number of tokens of the third group; and determines presence or absence of first and second digital watermarks on the basis of a first score obtained by a statistical test from the calculated numbers of tokens and a total number of tokens, and a second score obtained by a statistical test from the number of tokens of the first group.SELECTED DRAWING: Figure 1

The invention discloses a text watermarking method and device, a storage medium and electronic equipment, and relates to the technical field of artificial intelligence and digital watermarking, and the method comprises the steps: obtaining an original text and a watermark, inputting the original text and the watermark into an embedded watermark model, and carrying out the watermark embedding operation to obtain a watermark text. The watermark embedding model comprises a gating modification mechanism, the gating modification mechanism is used for replacing a segmented word with a threshold value greater than a preset threshold value in the original text with a candidate segmented word with the same meaning as the segmented word, the watermark is embedded in the candidate segmented word, and the threshold value is used for representing the feasibility of embedding the watermark at the position of the segmented word. According to the technical scheme, through the gating modification mechanism and the candidate word segmentation generation strategy, the text modification degree is strictly controlled, the semantic coherence of the text embedded with the watermark is improved, and the watermark robustness is improved.

The invention discloses a large model generation code watermarking method based on equivalent semantic transformation, a program, equipment and a storage medium, and belongs to the technical field of digital technology watermarking. According to the method, the equivalence principle of code semantic conversion is adopted, a conversion rule suitable for the generated code is designed, and the watermark is embedded into the generated code according to a certain coding rule by utilizing the rule. According to the method, the code generation model does not need to be retrained or finely adjusted, only the generated code needs to be post-processed, good cross-model compatibility is shown, and meanwhile, the watermark embedding efficiency is guaranteed. The method has the rapid watermark processing capacity, code watermark embedding can be completed in a short time, and the processing efficiency in practical application is greatly improved. Due to the fact that the equivalent semantic conversion mode is adopted, no negative influence is generated on the quality of the codes, and normal functions of the codes and readability of the codes are guaranteed.

The invention belongs to the field of digital watermarking technology and information security, and relates to an image blind watermarking method based on an exponential function. In most existing watermark embedding methods based on singular value decomposition, watermark information is often embedded through an additive method, and original watermarks or host information is often needed in the watermark extraction process. Based on the above problems, the invention provides a blind watermarking method based on an exponential function, and by means of the characteristics of the exponential function, a watermark and a host are respectively used as a cardinal number and an exponent of the exponential function, so that a carrier image containing the watermark can be obtained. In the extraction process, the watermark can be recovered by performing absolute value operation on the carrier image. The whole extraction process avoids the participation of original watermarks and hosts, realizes the blind extraction of the watermarks, and resists image processing attacks at the same time. The figure of the abstract is an embedded flow chart of the image blind watermarking method based on the exponential function, and the method is mainly applied to image copyright protection and information security.