139 results about "Information embedding" patented technology

The invention discloses a reversible information hiding method and system for enhancing image smoothness, and relates to the technical field of information security, and the system comprises a preprocessing module, an information embedding module and an optimization recovery module. According to the method, the Sobel operator is utilized to calculate the image gradient map, the region is divided based on the threshold T, the smooth region and the texture region can be accurately distinguished, an accurate basis is provided for differential processing of different regions, region characteristics are fitted, large-size blocks and sub-blocks of the smooth region are divided to facilitate subsequent refinement processing based on the gradient mean value, and the processing efficiency is improved. The small-size blocks of the texture area are beneficial to operation aiming at texture features, the rationality and effectiveness of overall processing are improved, meanwhile, the pixel mean value in the large-size blocks of the smooth area is calculated, the pixel and mean value difference value is recorded, reversible low-pass filtering is carried out, and the reversibility of the image in the whole information hiding and recovering process is ensured.

The embodiment of the invention provides an address data matching method and related equipment, and belongs to the technical field of geographic information services. The method comprises the following steps: constructing an address annotation corpus according to input address information data and a preset address database; the method comprises the following steps: generating a geographic information embedding vector according to a preset geographic information knowledge graph, performing address element analysis in combination with an address annotation corpus to obtain an address element sequence so as to construct a dictionary tree, and performing similarity screening through a spatial hierarchical matching algorithm to obtain a similar address set; generating an address embedding vector matrix through a preset word embedding vector model, and performing feature extraction through a preset semantic feature extraction model to obtain semantic-level similar features; according to input address information data, multi-dimensional character similarity matching is carried out to obtain character-level similar features, then weighted fusion is carried out in combination with semantic-level similar features, and target matching address data is determined according to a weighted fusion result. According to the embodiment of the invention, the address data matching accuracy and efficiency can be improved.

The invention discloses a generative image steganography method and device based on Stable Diffusion and discrete wavelet transform, and the method comprises the following steps: S1, sampling a potential representation of a to-be-generated image through employing a Stable Diffusion diffusion model, embedding secret information into a potential space of the diffusion model, processing the potential representation through employing discrete wavelet transform to carry out frequency domain information modulation, and carrying out the frequency domain information modulation; obtaining the potential representation after the secret information is embedded; s2, inputting the potential representation embedded with the secret information into a diffusion model, and generating a visual natural steganographic image through a diffusion inversion process; s3, inputting the received steganographic image into a diffusion model, and decrypting the received steganographic image without original model weight modification and empty prompt conditions to obtain embedded secret information; according to the method, the frequency domain embedding technology is combined with the potential diffusion model, so that high-fidelity, high-capacity and high-robustness image steganography is realized.

The invention discloses a heterogeneous network embedding method based on dynamic heterogeneous network decomposition and a hierarchical space-time attention mechanism. The method comprises the following steps: dynamic network decomposition: decomposing a heterogeneous network sequence into independent subgraph sets according to edge types; multi-order node attention aggregation: for each sub-graph, fusing information of nodes and different edge types; semantic level attention is embedded, wherein global semantic embedding is generated in combination with global relation type weight and meta-path long-short-term memory network coding; time dynamic modeling: respectively calculating a time attention weight and a Monte Carlo sampling approximate Horkes intensity, and then aggregating and splicing results of the time attention weight and the Monte Carlo sampling approximate Horkes intensity to obtain a final embedding result. According to the method, through cross-granularity semantic modeling and efficient time sequence dependence learning, an innovative solution is provided for node representation of the dynamic heterogeneous network.

The present application belongs to the technical field of image analysis, and particularly relates to a kind of electronic component defect detection method and system based on machine vision, comprising the following steps: S1, the visible light, X-ray and three-dimensional structure light image of the electronic component to be measured are acquired, and respective multi-scale feature pyramids are generated through independent feature extraction network respectively;S2, multi-modal fusion feature pyramid is generated;S3, candidate region generation network is applied to propose candidate defect region;S4, spatial topological relation graph is processed through graph neural network, and spatial context information is embedded into candidate defect region feature;S5, if the minimum distance of candidate region feature and all pre-stored prototypes is greater than the set discrimination threshold, then it is determined that the candidate region is unknown type potential defect.The present application widens the detection range and improves the robustness of feature expression, overcomes the limitation of insufficient information of single data source, and improves the accuracy, comprehensiveness and foresight of detection.

The invention discloses an emotion recognition method and system based on multi-modal data. The method comprises the steps that the multi-modal data in the task execution process of a target object is acquired; performing time dimension consistency processing on the audio data and the video data, and performing original feature extraction on the audio data and the video data which are consistent in time dimension to obtain multi-modal high-dimension original features; performing time sequence feature extraction on the multi-modal high-dimensional original features to obtain low-dimensional key time sequence features; performing cross-modal similarity calculation and adaptive fusion on the voice modal features and the visual modal features in sequence to obtain modal fusion features; performing dynamic gating adjustment and residual connection fusion on the modal fusion feature and the statistical information embedding feature of the target object in sequence to obtain a target fusion feature; and inputting the target fusion feature into an emotion recognition network for classification processing to obtain an abnormal emotion recognition result of the target object. According to the method, the accuracy of emotion recognition is improved.

The application discloses a kind of short video event detection method and device based on multi-modal representation learning, method includes: constructing latent sequence characteristic acquisition module, explores short video visual modal information and hearing modal information on the latent characteristics of short video before and after sequence;Construct a cyclic interaction information embedding module, construct a cyclic matrix for different modal, fully explore the relationship between multi-modal data and its coupling mode, mine the potential correlation between multi-modal feature elements;Local attention and global attention characteristic enhanced fusion feature representation are obtained by multi-modal attention fusion network;Event detection is realized using the short video multi-modal fusion features obtained by training.The application uses the visual and auditory modal information of short video, constructs short video feature representation learning network that fully mines the multi-modal information latent correlation characteristics and its attention enhancement, realizes the detection of short video event.Provides a new way of thinking for solving short video event detection problem.

The application discloses a neural network embedding method and device for power distribution network state estimation, electronic equipment and medium, wherein the method comprises: acquiring an input sequence; embedding time information and node type information into a vector through space-time prior information embedding to obtain a space-time embedding vector; sampling a node of interest according to a power flow direction of optimal power flow and fusing node information to obtain a node embedding vector; using a graph isomorphism neural network to capture the local of a graph and embedding it into a feature vector to obtain a structure embedding vector; fusing the input sequence and the three vectors and inputting them into a graph space-time prediction network to output a prediction result. Through the introduction of space-time prior information, graph node embedding based on the optimal power flow direction and graph structure embedding of the graph isomorphism neural network, the application realizes the modeling of the characteristics of the power distribution network, makes up for the deficiency of the prior art in the specific modeling of the power distribution network and improves the accuracy of the power distribution network state estimation.

This invention relates to a physical information neural network-based intelligent performance prediction method and system for automated multi-material fiber placement on complex curved surfaces in aircraft. The invention includes: collecting fiber placement process parameters; performing standardized preprocessing and physical constraint-enhanced sampling to obtain an enhanced training dataset; constructing a high-order nonlinear sparse regression candidate library based on the enhanced training dataset; jointly identifying piecewise smooth physical differential equation systems and their mode switching logic using sparse Bayesian regression and Hidden Markov Models to obtain an embeddable inverse mechanism model; constructing a Physical Information Embedded Generative Adversarial Network (PI-GAN); collaboratively optimizing the generator and discriminator through adversarial training to output surface defect indices and mechanical performance indices; and initiating incremental self-learning when introducing new process scenarios to achieve model self-evolution. This invention achieves end-to-end accurate prediction of multi-dimensional quality indices such as surface defects and mechanical performance.

The application discloses a system construction method for realizing CAD model security and data property right identification management, and realizes automatic identification management of UG / NX model security and data property right classification based on a PLM system.The technical scheme comprises the following steps: step 1: security classification label, that is, determining the security label of the model through a security review process node; step 2: intellectual property right classification label, that is, determining the intellectual property right label of the model through an intellectual property right review process node; step 3: specifying the corresponding label based on an improved document review process of product life cycle management of an enterprise; step 4: remotely transmitting the label information embedding program to a task queue server, that is, triggering the embedding program to remotely transmit the label information and related model information to the task queue server after the review is completed; and step 5: automatically writing the label into the server based on UG / NX secondary development technology, so as to write the label information into the UG / NX model.

The application discloses an image data hiding method for predicting pixels based on a voting strategy. The application divides a cover image into a gray area and a white area according to the pattern of an international chessboard, and uses the pixels of the white area to predict the pixels of the gray area. Here, the voting strategy is used for the prediction. Then, the application directly uses the predicted gray values to embed multi-bit secret data through a mapping table. When the secret data is recovered at the receiving end, the secret data embedded in the gray pixels can be inquired from the mapping table according to the difference between the received gray area pixel value and the predicted gray area value, so as to extract information. The scheme provided by the application combines the voting strategy with pixel prediction, can significantly improve the secret information embedding capacity, and enables the stego image to have a better visual effect when more information is embedded.

The invention relates to the technical field of data processing, and discloses a copper product full life cycle-oriented carbon footprint big data warehouse table design method, which comprises the following steps of: carrying out abnormal data filtering on an original carbon footprint data set of a copper product full life cycle to obtain standardized carbon footprint data; based on the standardized carbon footprint data, performing time series data storage optimization on the table structure of the big data warehouse to obtain a dynamic partition table structure; according to the standardized carbon footprint data, performing data mapping on the dynamic partition table structure to obtain an initial data table; traceability information embedding is carried out on the initial data table, and a carbon footprint big data warehouse table is obtained; performing storage structure reconstruction on the carbon footprint big data warehouse table to obtain an efficient storage data warehouse; carrying out life cycle carbon emission analysis on the efficient storage data warehouse to obtain a structured carbon footprint analysis report; according to the invention, the efficiency of carbon footprint data processing can be improved.

The application discloses a kind of environmental element information embedding heterogeneous network key node mining method, according to cross-layer rotation embedding, multi-modal environmental element embedding and reinforcement learning optimization strategy, the application not only can accurately model the cross-layer relationship of multilayer power network, improve the accuracy of edge prediction task, simultaneously can be combined with reinforcement learning to identify high-risk key nodes, provide scientific basis for disaster response, risk assessment and safety scheduling of smart grid, improve the security, resilience and intelligent level of power grid system.

The invention discloses a self-adaptive asymmetric image steganography method and system based on a diffusion model, and relates to the field of artificial intelligence and information security. According to the method, the corresponding cumulative distribution probability is calculated according to the noise to be embedded, then the secret information is embedded by slightly modifying the cumulative distribution probability, and the Gaussian noise after the information is embedded is obtained by using the inverse cumulative distribution function, so that the probability distribution of the embedded Gaussian noise is kept unchanged. Before actual embedding, simulation embedding is carried out in noise to be embedded, processes of simulation embedding + 1 and-1 are respectively completed to eliminate pixel points of which information cannot be correctly extracted, and modifications introduced by embedding + 1 and-1 to the noise are calculated. And then the asymmetric steganography embedding cost of each pixel point is obtained through calculation according to the modification amount, then secret information is embedded into cumulative distribution of Gaussian noise, and finally a reverse process is completed and a secret-carrying image is generated and obtained. The problem that an existing steganography method is insufficient in detection resistance and imperceptibility is solved.

The application relates to the field of information technology and provides a cross-modal document pre-training model implementation method and device.The purpose is to solve the problem that LayoutLMv3 cannot focus on a task, causing tasks to affect each other and leading to unsatisfactory performance of an ongoing task.The main scheme comprises the following steps: obtaining pre-training document image data;adding 2D text position embedding, 1D text position embedding and masked text information embedding, and taking the addition result as a to-be-fused text embedding vector;adding 2D image position embedding, 1D image position embedding and masked image information embedding, and taking the addition result as a to-be-fused image embedding vector;connecting the to-be-fused image embedding vector and the to-be-fused text embedding vector to obtain a multi-modal fusion embedding vector for pre-training model training; and through the pre-training model, different pre-training models are selected according to different task categories to perform fine-tuning training on different data sets, so that a model meeting a corresponding task is obtained.

The application discloses a target counting method and system based on image shape embedding, and relates to the technical field of image processing.The method comprises the following steps: obtaining an image to be counted and an example sample image; preprocessing the image to be counted and the example sample image; performing image segmentation processing on the preprocessed image to be counted and the preprocessed example sample image to obtain a first image block sequence corresponding to the preprocessed image to be counted and a second image block sequence corresponding to the preprocessed example sample image; inputting the first image block sequence and the second image block sequence into a counting model to perform target counting and obtain a target counting result; and the counting model adopts a visual transformer based on a class-independent counting framework, and the visual transformer comprises a self-similarity calculation module, a shape information embedding module and a density map regression module.The application realizes a class-independent counting task and significantly improves the accuracy and generalization ability of the counting task.

There is provided an audio signal signature module (500) comprising: an input (502) configured to receive a digital audio signal (121); and a block divider (130) configured to divide the received digital audio signal (121) into a sequence of audio blocks (131) having a block length. A signature unit (140) generates a signature (144) associated with the current audio block (131) by applying a private key (142) to the extracted audio feature (151). The combined information is provided as signed authentication information (141) and stored in a predetermined storage location in a memory (600). The predetermined storage location may be used as authentication access information (162) and a current audio block (131) embedded by an information embedder. An audio output (190) provides an authenticable audio signal (501) comprising a sequence of audio blocks with embedded authentication access information (162). A block divider (130) adjusts the block length of the audio block in accordance with the size of authentication access information to be embedded in the audio block.

This invention provides an online monitoring method and system for airborne flight runaway precursors of civil aircraft based on flight test data. The method includes: constructing a set of input flight parameters for identifying airborne flight runaway precursors; acquiring daily operational data and flight test data of the target aircraft model; and extracting physical feature information of airborne flight runaway by referencing the aerodynamic mechanism model and extreme flight envelope boundary of the target aircraft model. Based on the domain adaptation concept, an offline precursor recognition model is constructed that integrates physical feature information embedding, meta-learning, and multi-instance learning. Based on knowledge distillation technology, the recognition capability of the offline precursor recognition model is transferred to a lightweight network constructed from gated recurrent units to generate an online precursor monitoring model, enabling real-time precursor probability calculation and early warning. Finally, an intelligent agent model based on a dual-delay deep deterministic policy gradient algorithm is constructed to verify the effectiveness of the online precursor warning. This invention overcomes the cross-domain data gap and meets the requirements of online lightweight computation.

This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method of image processing includes receiving image data from an image sensor; receiving ranging data from a ranging sensor; embedding first spatial features of the image data with first temporal information associated with the image data; embedding second spatial features of the ranging data with second temporal information associated with the ranging data; determining first bird's-eye-view (BEV) features based on the first spatial features embedded with first temporal information; determining second BEV features based on the second spatial features embedded with second temporal information; and determining, based on the first and second BEV features, a feature set for processing by a transformer network. The feature set includes at least a portion of both the first and second BEV features. Other aspects and features are also claimed and described.

The application discloses a method for keeping encrypted frequency domain thumbnail with enhanced availability, which realizes the balance between image privacy and enhanced availability. The privacy means to protect the detail part of the image which may contain user privacy, and the enhanced availability means to record additional information as much as possible while keeping the original image thumbnail to maintain the general outline of the image. In the application, the image owner performs space operation on the original image in the frequency domain, then performs stream encryption on the low bits of AC coefficients in each block and keeps the original DC coefficients, so that the ciphertext image and the original image keep the effect of thumbnail approximation. After obtaining the ciphertext image, the information hider records additional information in the fixed position of each block in the frequency domain, and the thumbnail of the ciphertext image is not greatly affected. The image encryption and information embedding process of the scheme are completely reversible, and the image encryption and additional information hiding steps are separable.

The invention provides a converter steelmaking real-time carbon temperature prediction model, prediction method and system based on Vision Transform, and belongs to the field of converter steelmaking. In the prediction model, an image set is input through a first interface, an image convolution layer performs feature extraction to form an image block matrix, and classification lexical elements are added through an image embedding layer to generate an embedding matrix; outputting an image static coding classification lexical element matrix through a static feature encoder; the second interface inputs a carbon temperature value of a previous moment of the corresponding image set; weight parameters are given to the carbon temperature values through a historical information embedding module, coding and mapping are carried out, and then a historical embedding matrix is output by aligning dimensions; and the splicing module splices the image static coding classification lexical element matrix and the historical embedding matrix and adds dynamic feature classification lexical elements, the dynamic feature encoder outputs the dynamic coding classification lexical elements, and an MLP layer classification head generates and outputs a carbon temperature prediction value at the current moment. According to the method, the accuracy, the real-time performance and the continuity of a prediction result are improved.

The invention discloses a method for embedding secret information into a video, a video steganography method and a related device.According to the method, a two-dimensional array of a coding unit is constructed, original one-dimensional representation is expanded to a structured two-dimensional embedding space, the number of available candidate states of each embedding operation is remarkably increased, a foundation is laid for high-capacity embedding, and the embedding efficiency is improved. By introducing polygon division, region-based coding rather than point-based mapping is realized, and the expression capability of a prediction unit division mode is greatly improved, so that more secret information can be embedded under limited structure modification, and a replacement set of an element S is screened out through a position relationship between the element S and a coverage domain, and the prediction efficiency is improved. And determining a replacement element of the element S based on the distance and performing replacement to complete secret information embedding, so that distortion cost caused by different modification directions and structural disturbance can be simulated more accurately, the candidate replacement element is ensured to be kept within an acceptable distortion range, the minimum distortion principle can be forced to be executed, and the visual quality is effectively kept.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a mixed identifier generation type recommendation method and system based on local collaborative context, and the method comprises the steps: obtaining a user historical interaction sequence, and carrying out the multi-granularity clustering of users, and obtaining a group of each user; constructing a static identifier based on the article content, constructing a dynamic identifier in combination with the user group information and the local interaction sequence, and fusing to generate a mixed identifier of each article; constructing an instruction fine tuning task, embedding group information into an instruction prompt word, and learning by using a large language model to generate a mixed identifier of a next article from a historical sequence; and generating a recommendation result based on the optimized large language model according to the historical sequence of the target user and the group information thereof. According to the method, the local collaborative context is fused through multi-granularity group estimation, and the mixed article representation is constructed in combination with static and dynamic identifiers, so that the problems of single user interest modeling and semantic deficiency of article representation are effectively solved, and the accuracy and personalized level of generative recommendation are improved.

The application discloses a medical data desensitization and traceability auditing method based on differential privacy, and relates to the technical field of medical data security, and specifically comprises the following steps: collecting two types of medical data and standardizing the format, extracting the clinical decision weight of numerical data and the privacy risk label of text data, and then implementing differential desensitization based on the weight and the label, adding Laplace noise with correlation weight to the numerical data, replacing or masking the key information of the text data in stages, embedding a unique traceability identifier and verifying privacy compliance and availability, finally establishing a closed-loop auditing mechanism to support identifier extraction, compliance verification and leakage tracking, and continuously optimizing relevant parameters and strategies based on feedback, effectively guaranteeing data security and traceability.

The invention provides an image processing method and an image processing system, a brightness channel of an image is processed through local synchronization information and global synchronization information, and because the local synchronization information indicates a spread spectrum matrix translation and / or overturning rule, a plurality of first synchronization matrixes can be obtained; according to the method, the plurality of first synchronization matrixes are arranged in the image, and the whole image is paved with the plurality of first synchronization matrixes, for example, even if the image is cut, geometric distortion of the image can be recovered based on the first synchronization matrixes in other images, so that geometric attacks can be resisted; in addition, the position of each first synchronization matrix in the first image can be indicated through the global synchronization information, so that screen shooting attacks can be resisted through the global synchronization information. Therefore, the method provided by the invention can resist geometric attacks and screen shooting attacks at the same time, the robustness of watermark information embedding is improved, and thus the success rate of subsequent watermark extraction is also improved.

The application discloses a FDA-MIMO radar communication integrated transmitting beam forming method, comprising the following steps: constructing a FDA-MIMO radar communication integrated signal model; deducing a signal processing model of a communication receiving end; realizing communication information embedding based on beam pattern modulation through transmitting beam forming; designing a transmitting beam forming matrix to ensure radar detection performance; establishing a FDA-MIMO radar communication integrated transmitting beam forming optimization model; and solving the optimization model by using an obstacle function algorithm. The application not only satisfies the beam performance of radar detection, realizes multi-user communication information embedding, prevents a eavesdropping user from intercepting multi-user communication information at one time, but also applies the optimal transmitting beam forming matrix obtained by solving the optimization model to a FDA-MIMO-based radar communication integrated transmitting end, effectively improving the detection and communication performance of the radar communication integrated system.

A system for predicting a property of a material of the present invention may extract a graph embedding by inputting material information into a first artificial intelligence (AI) model, and extract a text embedding by inputting a text description of a crystal structure of the material into a second AI model, classify the text embedding into a plurality of structure information embeddings, and concatenate the graph embedding with at least one of the plurality of structure information embeddings, wherein the structure information embeddings may be classified to include global information, semi-global information, and local information of the crystal structure. The provided system and method may be employed to predict aspects of a crystal structure, a molecular structure, a protein structure, a catalyst structure, or a metal-organic framework of a target material, or to predict physical properties of a target material.

The invention discloses a generative image steganography method based on robust position mapping. According to the technical scheme, a steganography system composed of a run length coding module, an initial Gaussian noise generation module, a candidate pool processing module, a pseudorandom sequence generation module, an index sorting module, an information embedding module and a stable diffusion model SD is constructed. The run length coding module carries out run length coding on the secret information to obtain a two-tuple Y; an initial Gaussian noise generation module generates a Gaussian noise tensor zinit; the candidate pool processing module performs positive and negative value division on the zinit; the pseudo-random sequence module generates a random sequence P, and an index matrix S'is obtained through the index sorting module. The information embedding module embeds the secret sequence after run length coding in the Y into an empty tensor zemed to obtain a tensor zemed conforming to Gaussian distribution; and the SD carries out denoising on the zzzled and decodes the zzzled to generate a steganographic image. According to the method, the secret information extraction accuracy and steganography capacity can be effectively improved.

The present application relates to a kind of image classification methods based on separable convolution block and spatial reduction attention mechanism, belong to image classification field.Crossover depth separable convolution and improved spatial reduction attention mechanism are added to PVT model, reduce model training time, and while reducing attention calculation, the original information of feature map is not lost basically at the same time amount of calculation.Crossover depth separable convolution block embedding and spatial reduction attention mechanism based network model is built, including block embedding module, linear projection module, position information embedding module and spatial reduction attention mechanism module;Image classification is carried out to improve the calculation rate when image classification and preserve original boundary information, so as to achieve the overall improvement effect.The present application effectively improves the problems such as huge model calculation and image boundary information loss, reduces the amount of calculation of model and improves the model classification performance.

This invention provides a social network alignment method and system based on multi-information fusion and graph optimization, belonging to the field of internet social network technology. Based on constructed social network user graph data, it obtains user structural information, user behavioral information, and user attribute information. According to the constructed optimization model, it obtains the transmission matrices from the source network node structure embedding matrix to the target network structure information embedding matrix, behavioral information embedding matrix, and attribute information embedding matrix, respectively, and calculates the three node embedding similarity matrices of the source and target networks. Based on a gating mechanism, it obtains the fused embedding similarity matrix and acquires the user alignment nodes. This invention extracts the semantic features of users, reduces the sparsity of the network structure, eliminates the need for pre-training data, and is more suitable for the needs of large-scale networks. By learning user attribute features and network topology features, it reduces the interference of structural noise and attribute noise, improving the accuracy of cross-social network user alignment.