6results about How to "Increase imperceptibility" patented technology

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 relates to the technical field of data security, and discloses an image hiding method and system based on a local binary pattern, and the method comprises the steps: calculating a texture feature matrix of the local binary pattern of a plaintext image; generating a three-dimensional chaotic sequence according to the texture feature matrix; constructing a measurement matrix by using the three-dimensional chaotic sequence; performing two-dimensional compressed sensing processing on the plaintext image by using the measurement matrix to obtain a measurement value matrix after the plaintext image is compressed; performing scrambling and diffusion operation on the measurement value matrix to obtain a ciphertext image; the ciphertext image is embedded into the carrier image to obtain the encrypted carrier image, so that the known plaintext attack can be effectively resisted, the imperceptibility of the encrypted carrier image is improved, the blind extraction effect can be realized, and the efficiency and security of image protection are greatly improved.

The invention discloses a three-function deep counterfeit watermark evidence obtaining method, and relates to the technical field of digital image evidence obtaining and deep counterfeit detection. The method comprises the following steps of: 1, constructing a 152-dimensional composite watermark LIDMk; 2, constructing a four-module network architecture comprising an encoder E, a random operator factorization head decoder FHD and a discriminator D; 3, training the network architecture by adopting a two-stage training strategy; and step 4, applying the trained network architecture to deep counterfeiting evidence obtaining. According to the invention, through constructing a novel composite watermark and a unified network architecture, the integration of detection, tampering positioning and source tracing of deeply-forged contents is realized, meanwhile, the classical tradeoff problem of a watermark technology is solved, and the robustness and practicability of the evidence obtaining method are improved.

The embodiment of the invention provides a single-stage training method of an end-to-end digital watermark model, an image watermark identification method and related equipment. The training method comprises the following steps: positively inputting an image and watermark information to be embedded into a convolutional neural network and a first reversible neural network model; performing fusion processing; forward inputting into a second reversible neural network model, and adding noise in an actual scene based on second reversible neural network simulation to obtain a watermark image with noise; performing reverse input to the second reversible neural network model, performing reverse fusion processing, and performing reverse input to the first reversible neural network model and the convolutional neural network to obtain extracted watermark information and a recovered image; and training based on a first difference between the watermark information to be embedded and the extracted watermark information, a second difference between the first image and the watermark image with noise, and a third difference between the first image and the restored image to obtain application parameters of the trained end-to-end digital watermark model.

The present invention discloses a real-time screen watermarking method and system for tracing screen shooting leaks, which can be used to obtain evidence from secretly photographed screen photos. The method uses a preset watermark template composed of an information watermark and a synchronization watermark, and encodes key information such as a user ID and an IP address as watermark information into a corresponding watermark template through a QR code encoding method and an inverse Fourier transform. The least noticeable difference model is adopted to directly embed the pre-generated watermark template into the back buffer of the desktop window manager exchange chain, thereby ensuring the imperceptibility of the watermark and realizing the real-time refresh of the watermark. Automatic perspective correction is realized through a synchronous response indexing method, and the information watermark template embedding area is located. Finally, the information watermark is extracted from the discrete Fourier transform domain of the noise component. The method can effectively resist various screen secretly photographing methods, and realize the high-frequency refresh of the watermark and the robustness of watermark evidence without affecting the normal display of the screen.

The application discloses a frequency dynamic convolution-based anti-compression-encoding robust audio watermarking method and system, relates to the field of multimedia information security, and comprises the following steps: an audio frequency domain conversion step, a watermark encoding step, a watermark embedding step, an audio waveform reconstruction step and a watermark extraction step. The application improves the robustness of the watermark to various signal processing while ensuring the imperceptibility of the watermark, effectively solves the watermark failure problem caused by compression encoding in the traditional watermarking method, can better learn to embed the watermark in the robust and imperceptible area in the frequency domain, and enhances the copyright protection and traceability of the watermark in the real application scene with multiple compressions.