31 results about "Watermark robustness" patented technology

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 discloses a model watermark robustness enhancement method based on antagonistic immunity, which mainly solves the problem that the existing model watermark is difficult to resist various watermark removal attacks, and adopts the scheme that a public data set and a convolutional neural network suitable for an image classification task are respectively used as original data and a target model; training a target model by using the original data; taking the generation model as a watermark generation model and training the watermark generation model by using the original data; inputting the original data into the trained generated watermark model for forward propagation to output watermark data; splicing the original data and the watermark data, and inputting the spliced original data and watermark data into the trained target model for forward propagation to obtain a model watermark; tiny disturbance is added to original data to generate an adversarial sample, the adversarial sample is spliced with the original data and then input into the model watermark for robustness training, and the model watermark with enhanced robustness is obtained. The method can effectively enhance the generalization ability of the model watermark under different data distributions, improves the robustness of the model, and can be used in the fields of information security, financial services, medical care and the like.

The invention relates to the technical field of generative AI watermarks, and discloses a domestic credible watermark embedding and tracing method and system based on generative AI output. An acquisition module of the system acquires original content data output by a generative AI, and establishes a trusted watermark configuration database mapped with the original content data; the watermark target setting module configures a watermark embedding target based on the database and covers watermark robustness optimization, watermark invisibility optimization and watermark traceability association optimization; the watermark classification identification module determines target classification identifications, wherein the target classification identifications comprise a main watermark target, an auxiliary watermark target and a reference holding target; after the key watermark parameters are selected by the watermark influence analysis module, the influence analysis of the key parameters on the watermark embedding target is executed; the watermark traceability module establishes a watermark optimization constraint according to the influence analysis result, executes watermark parameter directional embedding, and completes localized credible watermark embedding; and the traceability path construction module generates a traceability path based on the embedding result to obtain a traceability verification result.

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 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 digital watermark generation and credibility verification method based on block chain salt value mapping. The digital watermark generation method comprises the steps that S1, a user submits copyright information to be embedded and an image file of the copyright information to be embedded; s2, carrying out uplink registration on the copyright information with the embedding, generating a 64-bit hexadecimal block chain, storing the 64-bit hexadecimal block chain in a local database, and generating an increment ID of the 64-bit hexadecimal block chain; s3, performing Hashids coding on the increment ID by taking the 64-bit hexadecimal block chain as a salt value to generate a 10-bit hexadecimal watermark load, and performing associated storage on the generated watermark load, the increment ID and the 64-bit hexadecimal block chain; and S4, embedding the watermark load into the image file in which the copyright information is to be embedded. According to the method, through a cooperation mechanism of the block chain and the local database, on the premise that the watermark load capacity is not increased and the watermark robustness is not affected, the embeddable copyright information capacity is improved, the copyright information can be effectively prevented from being tampered, and a reliable guarantee is provided for confirming copyright confirmation traceability.

The invention discloses an image watermark generation method and system based on a diffusion model optimization potential variable, and the method comprises the steps: training a secret encoder and a watermark extractor, and coding a message into a secret residual error; after the residual error is injected into the potential representation of the image, mapping the residual error into a potential vector with a watermark, which accords with the prior of a diffusion model, through DDIM inversion; performing iterative optimization on the potential vector by using a pre-trained diffusion model U-Net, introducing double constraints of image reconstruction loss and message fidelity loss in a denoising process, and generating an image with high visual quality and high watermark robustness; and finally, extracting the message through a watermark extractor and carrying out statistical significance verification to realize copyright authentication. According to the method, dynamic watermark embedding is achieved, the attack resistance is high, the image quality is well kept, an original model structure is not changed, and the method is suitable for copyright protection and user tracking of AIGC content.

The disclosure provides a feature point detection-based anti-screen camera robust watermark embedding, extraction method and system, relates to the technical field of digital media copyright protection, and when a carrier image is photographed and watermark is extracted, an image correction algorithm based on PCA and SURF is proposed, the generated distortion can be well corrected, and the robustness of the screen camera watermark is indirectly ensured. The image after the screen camera can be corrected to restore the image to the state in the screen, and the selection of the feature area before and after the correction is also performed to improve the embedding effect of the watermark. In order to improve the robustness of the screen camera watermark, in the embedding of the screen camera watermark, the carrier image is subjected to DCT transformation, and then experiments are designed to select the DCT coefficients of the embedded watermark. Thus, after the watermark is embedded, the robustness is high, and the invisibility is also high.

The application relates to the technical field of generative AI watermark technology, and discloses a localization trusted watermark embedding and tracing method and system for generative AI output. An acquisition module of the system acquires original content data of the generative AI output, and establishes a trusted watermark configuration database mapped with the original content data; a watermark target setting module configures a watermark embedding target based on the database, covering watermark robustness optimization, watermark invisibility optimization and watermark tracing correlation optimization; a watermark classification identification module determines a target classification identification, including a main watermark target, an auxiliary watermark target and a benchmark maintenance target; after a key watermark parameter is selected by a watermark influence analysis module, the influence of the key parameter on the watermark embedding target is analyzed; a watermark tracing module establishes watermark optimization constraints based on the influence analysis result, performs directional embedding of the watermark parameter, and completes localization trusted watermark embedding; and a tracing path construction module generates a tracing path based on the embedding result, and obtains a tracing verification result.

The invention provides an audio watermark embedding method and device and a conference system, and relates to the technical field of digital watermarking. And obtaining an original audio collected from an environment where the terminal is located in the first time period, wherein the original audio comprises an audio played by the terminal in the first time period. The playing audio is configured to be embedded with a watermark based on the first watermark embedding parameter value by using a first watermark embedding algorithm. And carrying out watermark extraction on the original audio according to the first watermark embedding algorithm. And if the watermark extraction from the original audio fails, embedding the watermark in the audio to be played of the terminal based on the second watermark embedding parameter value by using the first watermark embedding algorithm to obtain a new played audio. Under the condition that the watermark cannot be successfully extracted from the original audio, the watermark embedding parameter value is adjusted, so that the watermark embedded in the audio played later by the terminal can adapt to the environment and is successfully extracted, the adaptive environment adjustment of the audio watermark is realized, and the robustness of the audio watermark is improved.

This invention discloses a method and apparatus for evaluating the robustness of image watermarking. The method includes: acquiring an original image dataset; printing watermarks on a portion of the original images in the original image dataset using the watermarking algorithm to be evaluated, forming a watermarked image dataset; attacking all images in the watermarked image dataset to obtain an attacked image dataset; performing watermark recognition on all images in the attacked image dataset to obtain watermark recognition results; the watermark recognition results include the true positive rate and the false positive rate; using a preset image quality measurement algorithm, calculating the image quality measurement values ​​before and after the attack for each watermarked image, as the image quality degradation result; determining the image watermark robustness evaluation result based on the watermark recognition result and the image quality degradation result; the image watermark robustness evaluation result includes an image watermark robustness evaluation report. This invention provides a complete, systematic, and mature method for evaluating watermarking algorithms.

The invention discloses an adaptive code watermark embedding method and system based on real-time feedback, and the method comprises the steps: obtaining a currently generated code sequence in real time in a code generation process; inputting the obtained current code sequence into a pre-trained lightweight naturalness discriminator module to obtain a score reflecting the naturalness of the current code; inputting the obtained naturalness score into a dynamic bias controller module, presetting a target naturalness in the module, and calculating a watermark bias value; inputting the watermark offset value into a watermark strategy and code generation module, and generating new probability distribution after self-adaptive watermark offset adjustment; in the new probability distribution, through a lightweight naturalness discriminator, a new round of evaluation and control circulation is carried out by using the newly generated Token, a real-time feedback control loop including sensing, decision making and execution is constructed, the watermark bias strength is dynamically adjusted according to the real-time generation quality, and dynamic balance of watermark robustness and code naturalness is ensured.

The invention relates to a watermarking method for a text-generated image model, which comprises the following steps: obtaining a trigger prompt word, a watermarking image and a text-generated image model, the text-generated image model comprises a text encoder, an image auto-encoder and a UNet; adding a Token index for triggering a cue word in a predefined dictionary of the text encoder; freezing all components except a text encoder in the text generation image model, and training parameters related to triggering cue words in the text encoder through a self-adaptive adjustment strategy; freezing the text encoder and the image self-encoder, and adjusting parameters of UNet through iterative training so as to bind the trigger prompt word with the watermark image; and watermark robustness is improved through adversarial training, and watermark embedding of the text generation image model is completed. Compared with the prior art, the method can accurately realize the ownership authentication of the text generated image model.

The invention provides an audio watermark embedding method and device, electronic equipment and a storage medium, and belongs to the technical field of audio processing, and the method comprises the steps: obtaining the amplitude spectrum characteristics of an original audio signal, and determining a watermark embedding energy mask; inputting the amplitude spectrum features into a plurality of mask prediction network models to obtain watermark embedding risk masks; determining a target watermark embedding risk mask according to the watermark embedding risk mask to generate a frame-level embedding mask; and embedding the watermark information into the feature component corresponding to the target audio frame to obtain a watermark-containing amplitude spectrum feature, and performing inverse time-frequency transformation on the watermark-containing amplitude spectrum feature to obtain a watermark-containing audio signal. According to the method, the audio energy and the model prediction risk are combined, the audio frame which is most suitable for embedding the watermark is adaptively screened out, modification in hearing sensitive frames such as silent frames and transient frames is avoided, the problem that the audio quality is reduced due to undifferentiated embedding of the watermark is solved, and on the premise that the watermark robustness is not sacrificed, the watermark embedding efficiency is improved. And the imperceptibility of the watermark and the auditory quality of the audio containing the watermark are obviously improved.

The present invention discloses a video watermarking method based on a reversible neural network. The method constructs a watermark embedding and extraction network, comprising a watermark embedding module and a watermark extraction module that share network parameters, and a distortion module for simulating the HEVC compression effect. The watermark embedding module, the watermark extraction module and the distortion layer in the distortion module are all based on the reversible neural network. The distortion layer is pre-trained based on a pre-training set, and after the distortion layer pre-training is completed, the network parameters of the distortion layer are fixed. The watermark embedding and extraction network is then trained based on the training set, and after the watermark embedding and extraction network training is completed, a trained watermark embedding and extraction network is obtained. During testing, the test set is input into the watermark embedding module to obtain a watermarked video, HEVC compression is performed on the watermarked 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 large watermark embedding capacity, good watermark robustness and high watermark extraction accuracy.

This invention discloses a robustness enhancement method for model watermarking based on adversarial immunity, primarily addressing the problem that existing model watermarking methods are unable to resist various watermark removal attacks. The solution involves: using a public dataset suitable for image classification tasks and a convolutional neural network as the original data and target model, respectively; training the target model with the original data; using a generative model as the watermark generation model and training it with the original data; inputting the original data into the trained watermark generation model for forward propagation to output watermark data; concatenating the original data and watermark data and inputting the concatenated data into the trained target model for forward propagation to obtain the model watermark; adding minor perturbations to the original data to generate adversarial examples, concatenating these examples with the original data, and inputting them into the model watermark for robustness training, resulting in an enhanced robust model watermark. This invention effectively enhances the generalization ability of model watermarking under different data distributions, improves model robustness, and can be applied in many fields such as information security, financial services, and healthcare.

A gloss value of a clear topcoat (e.g., flood of varnish) on a substrate relative to a bare substrate is an indicator of digital watermark robustness for an encoded signal carried by modulating the clear topcoat. This measurement can be used to predict encoded signal robustness prior to printing. One claim is directed to an image processing method comprising: obtaining a first gloss value corresponding to a layer comprising a matte or gloss topcoat flooded over a substrate; obtaining a second gloss value corresponding to the substrate; and determining a relationship between the first gloss value and the second gloss value to determine digital watermark signal robustness of a sparse mark pattern provided on the substrate with the matte or gloss topcoat. Of course, other claims are combinations are described as well.

The application discloses a watermark-based audio anti-recording method, medium, equipment and product, and relates to the field of audio processing.The method comprises the following steps: modeling nonlinear effects on audio containing watermarks, modeling channel distortion on audio signals, weighting and adding the nonlinear demodulated signals and the distorted analog signals to obtain initial analog recording audio signals; modeling time dislocation and editing operations to obtain analog recording audio signals containing watermarks; modulating the watermark signal energy after recording distortion training to a near ultrasonic frequency band, superimposing the audio signals and the modulated watermark signals in the frequency domain, and obtaining the final watermark audio signals.The maximum time dislocation based on time dislocation modeling sets the step of the sliding window, the final watermark audio signals are scanned and the spectrum diagram is obtained, the watermark features are extracted from the spectrum diagram, and the final watermark sequence is generated by decoding the watermark features.The application can reduce the recording audio quality and improve the watermark robustness.

The invention discloses a single-frame video non-blind watermark embedding and detecting method based on DWT-SVD (Discrete Wavelet Transform-Singular Value Decomposition). The watermark embedding method comprises the following steps: S1, converting an original video frame into a YCbCr color format, and performing secondary discrete wavelet decomposition on a Y component to obtain a low-frequency component LL; s2, dividing the LL into a plurality of isometric matrixes LLi, performing singular value decomposition on each matrix LLi, and arranging singular values in a descending order to obtain a singular value diagonal matrix S; s3, modifying S to obtain S * according to watermark information to be embedded, performing inverse SVD decomposition to obtain LLi *, and merging to obtain LLi *; and S4, replacing the original low-frequency component LL with the watermark-embedded low-frequency component LL *, performing secondary discrete wavelet inverse transformation to obtain Y *, replacing the original Y component, converting into an original color format, and outputting the watermark-embedded video frame. According to the method, the defects of poor watermark robustness and concealment of the existing single-frame video are overcome, and the detection accuracy is further improved.

The invention discloses an image watermarking method based on human visual characteristics, which relates to the technical field of information security, and comprises the following steps: respectively converting watermark information and an original image into Lab color space, processing the original image by using an LBP operator, and layering by using texture complexity; carrying out DCT (Discrete Cosine Transform) on the three-component graph of the layer of image with higher complexity, calculating the angular frequency of the three-component graph, and calculating a contrast perception threshold value by utilizing a CSF model to obtain the maximum amount, namely the threshold value, of the embedded watermark under each frequency; performing compromise adjustment to balance the watermark embedding strength K value of each sub-block, enabling the watermark capacity to be close to a threshold value, and embedding the watermark according to the K value, the watermark information and the threshold value; carrying out inverse DCT and color space transformation on the watermarking-containing graph, and synthesizing the watermarking-containing graph with the three-component graph without the watermarking to obtain a watermarking-embedded graph; and finally, according to the K value, carrying out reverse processing on the original image and the image containing the watermark, and extracting the watermark. According to the method, the watermark transparency is better guaranteed, the watermark capacity is increased, and the watermark robustness is guaranteed.

The invention discloses a customized generative model copyright protection method based on robust watermark optimization, and the method comprises the steps: embedding a randomly generated initial watermark sequence into a basic generative model parameter, and obtaining an initial generative model with a watermark; taking the initial model as a proxy model weight, and constructing a proxy customization model to simulate an attacker customization training process; utilizing the proxy model to simulate fine tuning training on the customized data set; constructing a watermark robustness optimization target based on a gradient, and introducing a Fisher information matrix to carry out weighted constraint on a watermark updating gradient, so that a watermark parameter, namely, a robust watermark subspace, is iteratively optimized; after multiple rounds of proxy training and watermark updating are alternated, an optimized robust watermark is obtained and is re-embedded into the basic model to be published externally, and the robust watermark is used for any downstream customized generation task; in the verification stage, an image to be detected is input into the pre-training extraction network, watermark information is extracted and matched with an original watermark, and model verification and copyright evidence obtaining are completed.

The application provides an audio watermark embedding method and device, electronic equipment and storage medium, and belongs to the technical field of audio processing. The method comprises the following steps: obtaining the amplitude spectrum feature of an original audio signal and determining a watermark embedding energy mask; inputting the amplitude spectrum feature into a plurality of mask prediction network models to obtain a watermark embedding risk mask; determining a target watermark embedding risk mask according to the watermark embedding risk mask to generate a frame-level embedding mask; embedding watermark information into the feature component corresponding to the target audio frame to obtain a watermark-containing amplitude spectrum feature, and performing inverse time-frequency conversion on the watermark-containing amplitude spectrum feature to obtain a watermark-containing audio signal. The application combines audio energy and model prediction risk to adaptively select the most suitable audio frame for embedding a watermark, avoids modification in silent, transient and other auditory sensitive frames, solves the problem of audio quality degradation caused by indiscriminate watermark embedding, and significantly improves the imperceptibility of the watermark and the auditory quality of the watermark-containing audio without sacrificing the robustness of the watermark.

The invention discloses an endogenous watermark model and method combined with a low-cost watermark enhancement module, and the method comprises the steps: introducing a watermark enhancement module into a shallow layer and a deep layer of a decoder respectively in the reconstruction process of the decoder after an image is mapped to a potential space by an encoder. A watermark redundancy enhancement module is adopted in the shallow layer, and fusion of watermark features and latent variables is enhanced through repeated operation and sub-pixel convolution; a latent variable guided watermark enhancement module is adopted in the deep layer, and a low-interference watermark residual error is generated through bilinear interpolation and low-cost convolution. According to the method, the watermark robustness is remarkably improved while the quality of the generated image is ensured. ResNet is adopted as a watermark extractor, and combined training is carried out by combining MSE, LPIPS, WGAN and BCE loss functions. According to the invention, high-fidelity and high-robustness watermark embedding and extraction are realized.

The invention discloses an image protection method for balancing robustness and generation diversity. The method comprises the following steps: encoding a randomly generated random session key by adopting a preset master key to obtain a random session key code; encoding a watermark to be embedded for image protection by using the random session key to obtain a watermark code; and generating a watermark noise vector based on the random session code and the watermark code, and embedding the watermark noise vector into a to-be-protected image. According to the method, a robust and hidden generation image detection and traceability scheme is realized, so that the watermark embedded in the image can give consideration to the robustness of the watermark and the diversity of image generation.

The invention provides an anti-screen shooting image watermark network method and system based on double-domain fusion and frequency domain attention. The method comprises the following steps: step 1, preprocessing binary watermark information; step 2, constructing a double-domain collaborative high-frequency residual error enhancement network DRHE-Net; 3, constructing a real noise-containing watermark image for network training; step 4, establishing a frequency domain attention decoding network matched with the encoder; and step 5, a weighted multi-objective loss function is adopted to complete network end-to-end training, and balance between imperceptibility and robustness of watermarks is realized. According to the method, the watermark information and the carrier image are processed by using the double-domain collaborative residual enhancement network and the frequency domain attention mechanism, and accurate balance between watermark robustness and imperceptibility in the screen shooting scene is realized.

The invention relates to a reversible blind watermark robustness improving method based on a Swindow-Transform. The method comprises the following steps: extracting multi-scale features of an input image by using the Swindow-Transform; generating a reversible blind watermark according to the multi-scale features, and embedding the watermark into the input image; selecting noise by using the dynamic noise layer and adding the noise to the input image to generate a distorted image; the method comprises the following steps: extracting multi-scale features of a distorted image by using a Swindow-Transform, and recovering an embedded reversible watermark by using a decoder; performing feature recovery on the multi-scale features according to the reversible watermarks; performing self-adaptive feature recoding of the watermark on the feature of the feature recovery by using an encoder, and iterating to set the number of times; and when the loss function value is greater than the loss function value of the previous iteration, adjusting the embedding parameter and the feature recovery parameter of the watermark and the fusion parameter of the multi-scale features of the distorted image. Through the design of the dynamic noise layer and in combination with the Swindow-Transform, various distortion types can be effectively dealt with, the robustness of the watermark is enhanced, and the extraction precision of the watermark is improved compared with the prior art under geometric distortion and non-geometric distortion.

The invention provides a robust watermarking method for a three-dimensional deformable grid model. The robust watermarking method comprises the following steps: calculating a grid saliency map of a three-dimensional grid model by utilizing a saliency map calculation module; encoding the watermark information into a watermark vector through a full connection layer by using the watermark information encoding network, and splicing the watermark vector with the original three-dimensional model to form a fused matrix vector; encoding a matrix vector fused by the watermark information and the three-dimensional grid model by using a watermark embedding network, and guiding a watermark embedding process through a grid saliency map; obtaining a three-dimensional grid model with a watermark signal; utilizing an attack network to simulate various attacks on the three-dimensional grid model embedded with the watermark; and extracting watermark information from the three-dimensional grid model under various attacks by using a watermark extraction network. According to the method, through significance mapping of the three-dimensional grid, the watermark embedding strength can be adjusted, the invisibility of the watermark is remarkably improved, and meanwhile various attacks can be resisted. According to the method, relatively high watermark robustness and relatively small watermark embedding distortion are realized.

The invention discloses a large language model watermark embedding and detecting method based on attention head perception, and relates to the technical field of natural language processing and digital watermarking. The method comprises the following four core steps of: firstly, screening an attention head set which plays a key role in factual information coding through a'pure-pollution-recovery 'three-stage causal intervention experiment; secondly, a binary linear fact detector is trained based on the set, and accurate distinguishing of token factuality and non-factuality is achieved; then, in a model reasoning stage, a dynamic green list is only generated for non-factual tokens, watermark hidden embedding is completed through logits bias intervention, and bias intensity is adaptively adjusted along with text fact density; and finally, in a detection stage, reproducing an embedding process to screen non-factual tokens, and quantifying an abnormal degree by counting a matching ratio and a z score to realize accurate detection of the watermark. The text factual accuracy is prevented from being damaged through the attention head perception mechanism, the watermark robustness and concealment are improved through the dynamic green list design, the method is suitable for scenes such as copyright tracing and authenticity verification of the text generated by the large language model, and the engineering feasibility is high.

The invention discloses an image processing method and device, equipment, a storage medium and a program product, and is applied to the technical field of image processing. The method comprises the following steps: acquiring a first image and digital watermark content; the first image is input into a watermark calibration model, an image content confidence degree mask image of the first image output by the watermark calibration model is obtained through the watermark calibration model, and the image content confidence degree mask image is an image used for representing the attention degree of the watermark calibration model to different areas in the first image; according to the image content confidence mask image, the position where the digital watermark content is embedded into the first image is determined, and the value of the attention degree corresponding to the first position in the image content confidence mask image is larger than or equal to the value of the preset attention degree; and based on the first position, performing fusion on the digital watermark content embedding and the first image to obtain a second image after the digital watermark content is embedded into the first image. In this way, the watermark robustness is improved by dynamically selecting the high-robustness embedding position.

The invention discloses a digital sound watermark processing method and device based on echo hiding and electronic equipment, and relates to the technical field of digital watermarking. According to the core scheme of the digital sound watermark processing method based on echo hiding, a first audio signal is segmented through duration, then the energy of each segment is calculated, watermarks are embedded into the segments with high energy through an echo hiding method, and watermarks are not embedded into the segments with low energy. In this way, the shielding effect of human auditory sense is utilized, that is, the strong signal can cover the adjacent weak signal, so that the imperceptibility after the watermark is embedded is improved while the robustness of the watermark is ensured, that is, the relatively high audio subjective quality is kept. Meanwhile, an effective way is provided for controlling the embedding capacity of the watermark.