28 results about "Perceptual hashing" patented technology

A system for the tamper-proof verification of digital media, comprising the following functions: a) generating a cryptographic pattern by a physical device, wherein the pattern is derived from a timestamp, a device identification, and a random value, and signed using a cryptographic signature procedure with a non-extractable key stored in a security element; b) transmitting the pattern by the physical device using a physical signal that is not perceptible or only partially perceptible to humans; c) capturing the pattern by a digital capture device during the recording of a digital medium; d) recognizing and extracting the pattern from the recorded digital medium by an application; e) calculating at least one cryptographic hash and at least one perceptual hash of the digital medium; f) anchoring the verification data in a tamper-proof data storage device;g) Verification of the digital medium by comparing the calculated hashes with the anchored values ​​and checking the match of the extracted pattern with the generated pattern.

The invention provides a multi-dimensional construction data storage system and method, relates to the technical field of data storage, and realizes significant improvement of construction data storage efficiency and breakthrough of intelligent management capability by introducing multi-dimensional feature extraction, resolution factor calculation and a dimension perceptual hash algorithm. Firstly, original feature values of four dimensions are extracted from each construction data unit, standardized coding processing is carried out, a unified dimension code is generated, the precision degree and the association strength of data are quantitatively evaluated by calculating a time resolution factor, a space resolution factor and a coding similarity factor, and therefore a storage strategy is dynamically adjusted, and the storage efficiency is improved. According to the dimension perceptual hash algorithm, logic storage coordinates are intelligently generated through the factors, data with similar characteristics are naturally gathered on physical storage, data items are temporarily stored by maintaining an ordered structure on the memory management level, sorting is conducted according to the logic coordinates, disk random write-in operation is effectively reduced, and write-in performance is improved.

The application relates to a local geometric fine correction method suitable for high-resolution remote sensing images, relates to the field of remote sensing image geometric fine correction, and solves the problems that the existing method is calculated for the whole image, time and calculation resources are wasted, error matching is prone to occurring by using the existing matching method, geometric fine correction fails and the like, the rational polynomial RPC coefficient of the whole image is used for calculating the longitude and latitude range of the whole image; the longitude and latitude of a local area interested by a user are used for generating an image of the local area with rough geographic coordinates by using an indirect resampling method; a perceptual hashing algorithm is used for estimating the rough coordinate offset between the local area image and a reference base map, the part of the image corresponding to the local area image is cut from the reference base map, and the local area image to be processed is registered with the local reference base map by using Fourier transform blocking. The method has significant improvement in speed, accuracy and stability, and is suitable for various meter-level and sub-meter-level high-resolution remote sensing images.

The invention discloses a secrecy inspection method and system based on an OCR (Optical Character Recognition) edge server, and belongs to the technical field of computer and information security. The method comprises the steps of obtaining a target image, calculating a perceptual hash value of the target image, and performing preliminary screening on the target image through a built-in lightweight OCR model; and generating sensitive file alarm information of the target image based on a preliminary screening result, or uploading the target image and the perceptual hash value thereof to an edge server for processing, performing duplicate checking identification on the target image by the edge server based on the perceptual hash value, and if a duplicate checking identification result is that historical repeated data exists, sending the sensitive file alarm information to the target image. If yes, a perceptual hash value corresponding to the historical repeated data is used as an identification result to be returned to the terminal equipment, otherwise, a precise OCR model is called to carry out full-text identification on the target image, and the identification result is returned to the terminal equipment; and the terminal device generates sensitive file alarm information of the target image in response to the identification result. According to the invention, the throughput and response speed of the system can be effectively improved on the premise of ensuring the security of confidentiality check.

The application provides a robust watermark and password signature joint embedding and proving method and device for AIGC images, and the method comprises the following steps: constructing a structured watermark load and performing attack-resistant redundant coding to obtain a redundant load sequence, and calculating a first hash value of the structured watermark load; embedding the redundant load sequence into a hidden space domain feature of an AIGC generation model at at least one preset embedding stage of intermediate features of the AIGC generation model to generate an initial AIGC image carrying a hidden space domain watermark; embedding a watermark synchronization code, load backup information and a signature record identifier in medium and low frequency coefficients of frequency domain coefficients obtained after frequency domain transformation of the initial AIGC image to generate an intermediate AIGC image carrying a cross-domain double-layer collaborative robust watermark; and binding a password signature, a reference perceptual hash value and the signature record identifier to a file structure of the intermediate AIGC image and / or an external notarization record to generate a target AIGC image carrying the cross-domain double-layer collaborative robust watermark and the password signature.

The invention relates to the technical field of image processing and computer vision, in particular to a general image comparison method and device combining high-level semantics and low-level features. The method comprises the following steps: acquiring an image object to be compared, and setting a similarity threshold value; a pre-trained ResNet model is adopted to extract high-level semantic features of image objects, cosine similarity is calculated, and candidate image pairs are obtained through preliminary screening; respectively processing and comparing the candidate image pairs by utilizing perceptual hash, average hash, mean square error and structural similarity comparison algorithms, and screening potential image pairs again; according to a preset weight distribution scheme, performing weighted average on the plurality of similarities obtained by the plurality of algorithms of the potential image pair to obtain a final similarity; finally, the images corresponding to the similarity greater than the similarity threshold are similar image pairs. The method effectively solves the limitation of contrast, brightness, rotation, zooming and the like in the prior art, and can be widely applied to scenes such as document similarity detection, copyright protection, content auditing and the like.

The application discloses a similar image search method, system and device based on perceptual hashing and a medium thereof. The method comprises constructing a multi-scale space for an input initial image and generating a Gaussian pyramid based on the multi-scale space, obtaining a Gaussian difference pyramid based on the Gaussian pyramid, screening stable key points in the Gaussian difference pyramid, delimiting a densest region in the initial image and intercepting a minimum circumscribed rectangle as a target screenshot image according to the distribution density of the screened stable key points, extracting multi-modal features of the initial image, performing feature splicing to generate a joint feature vector, preprocessing the initial image and the target screenshot image, generating corresponding hash strings based on the preprocessed images and the joint feature vector, comparing hash strings of images to be searched with a hash set in a database, and outputting images with a hamming distance less than or equal to a preset threshold as similar results. The application improves the search accuracy of screenshot images to obtain complete original image information.

The invention relates to an image data intelligent enhancement method and system based on a large model, and the method comprises the following steps: S1, obtaining power transmission line hardware fitting image data collected based on an unmanned plane, carrying out the image segmentation through employing a Mask R-CNN, and removing invalid images; s2, identifying and repairing abnormal images with overexposure and underexposure by analyzing brightness histogram distribution characteristics of the images; s3, adopting a bilinear interpolation algorithm to adjust all images in the brightness correction image set into a unified target size, and adopting a perceptual hash algorithm to combine with cosine similarity calculation to identify and remove repeated images to obtain a standardized de-duplicated image set; s4, carrying out ControlNet-based conditional image generation enhancement on the standardized de-duplicated image to obtain an enhanced image set; and S5, for the enhanced image set, using a pre-training detection model to generate an initial annotation, and combining manual calibration to obtain a complete annotation data set. According to the invention, the hardware fitting damage detection precision and efficiency are effectively improved.

The invention provides a duplicate removal and compliance verification method and system for a secret evaluation evidence picture. The method comprises the steps of S1, secret evaluation evidence picture obtaining and preprocessing, S2, double-fingerprint duplicate removal processing, S3, multi-dimensional compliance verification processing, S4, compliance problem grading processing and S5, national secret SM4 encryption storage processing. By establishing a double-fingerprint duplicate removal mechanism combining perceptual hash and national secret SM3, accurate identification and accurate verification of similar graphs are realized, and national secret encryption compliance requirements are met; linkage is formed by constructing an automatic compliance verification processing flow and a duplicate removal flow, and full-flow automatic processing of the secret evaluation evidence picture is achieved; and storage compression is realized by reserving an original image and combining reference information replacement, and encryption and packaging are performed by adopting a national cryptographic SM4 algorithm, so that the security and effectiveness of cryptographic evaluation data are guaranteed. The method has the advantages of high duplicate removal precision, compliance verification meeting the secret evaluation requirement, high secret evaluation processing efficiency, high security and secret evaluation compliance, low storage cost and clear data association.

This application relates to the field of hybrid coding in video engines, providing a hybrid coding processing method, system, and device based on a video engine. This includes intelligently switching between H.265 inter-frame predictive coding and MJPEG intra-frame compression coding modes by dynamically monitoring the proportion of moving areas in the video frame. When the scene is static, discrete cosine transform is used to compress spatial redundancy; when there is rapid motion, motion vector compensation is used to eliminate temporal redundancy. Simultaneously, pixel difference analysis and perceptual hashing algorithms are used to dual-filter keyframes, eliminating visually redundant duplicate frames. Finally, a timestamp indexing system is used to establish spatiotemporal correlation between the optimized dual-stream data, achieving efficient storage and accurate reconstruction of the hybrid bitstream. This solves the problems of low coding efficiency, lack of adaptive coding modes, unoptimized redundant frames, and insufficient bitstream management.

The invention discloses a red-top crane behavior pattern recognition method and system based on an artificial intelligence visual technology, and relates to the technical field of intelligent recognition, and the method comprises the following steps: S1, collecting original video data containing the activity of a red-top crane through a high-resolution camera device arranged in a natural reserve; and S2, preprocessing and labeling the original video data: performing frame splitting according to a fixed time interval, eliminating redundant frames by using a perceptual hash algorithm, and performing bounding box labeling and behavior label labeling on red-crowned crane individuals in key frames. According to the automatic identification method for the behavior pattern of the red-top crane, by integrating advanced artificial intelligence and computer vision technologies, the efficiency and accuracy of behavior monitoring of the red-top crane are remarkably improved, the defects that traditional manual observation is greatly limited by the environment and low in efficiency are overcome, all-weather and all-around automatic monitoring is achieved, and the method is suitable for popularization and application. And the fineness and adaptability of behavior recognition are improved.

The invention discloses a field dynamic label identification method and system based on electronic screen display, and relates to the technical field of industrial Internet of Things and edge computing. The method comprises the following steps: a background service system performs structured packaging and encrypted signature on change information and then releases the change information; the edge gateway subscribes and verifies the decryption event message, and converts the decryption event message into a specific terminal updating instruction according to a preset rule; after the electronic identification terminal receives the instruction, a built-in rendering engine is combined with a display template to dynamically generate bitmap data, content differences are compared based on a perceptual hash algorithm, a local or global refreshing strategy is selected according to change areas and service priorities, and the power consumption state is adjusted in a linkage mode; and the terminal sends a confirmation message to the edge gateway after finishing refreshing, the edge gateway summarizes the state and reports the state to the background, and closed-loop feedback is formed. According to the invention, high-efficiency data transmission, flexible display adaptation, high safety, low-power-consumption operation and maintainable operation are realized, and the system is suitable for industrial sites, intelligent warehousing and other scenes.

This invention provides a method and system for deduplication and compliance verification of confidential review evidence images, including S1. Acquisition and preprocessing of confidential review evidence images, S2. Dual fingerprint deduplication processing, S3. Multi-dimensional compliance verification processing, S4. Graded processing of compliance issues, and S5. Encrypted storage processing using the national cryptographic standard SM4. By establishing a dual fingerprint deduplication mechanism combining perceptual hashing and the national cryptographic standard SM3, accurate identification and verification of similar images are achieved, meeting national cryptographic compliance requirements. An automated compliance verification process and deduplication process are linked to achieve fully automated processing of confidential review evidence images. Storage compression is achieved by retaining the original image and replacing it with reference information. Encryption and packaging are performed using the national cryptographic standard SM4 algorithm to ensure the security and validity of confidential review data. This method features high deduplication accuracy, compliance verification tailored to confidential review needs, high processing efficiency, strong security and compliance, low storage cost, and clear data correlation.

The invention discloses a lightweight road defect detection method based on an asymmetric co-evolution framework, and relates to the technical field of deep learning, and the method comprises the steps: selecting original road defect images, a depth map corresponding to each original road defect image, single-view depth data generated based on a DepthAnythingV2 model, and a real semantic segmentation image, and constructing a training set; building an asymmetric co-evolution framework comprising a teacher network, a student network, an information flow fusion module, a geometric perceptual hash injection module and an anti-fact trajectory backtracking module; performing data enhancement on the training set and inputting the data into an asymmetric co-evolution framework for training until the network converges to obtain a trained road defect detection model; and inputting a to-be-detected road defect image into the trained road defect detection model, and outputting a road defect semantic segmentation prediction map. Road defect detection is carried out based on the asymmetric co-evolution framework, and the detection efficiency and accuracy are improved.

This invention discloses a method and system for e-commerce platform transaction data storage and tamper-proofing, relating to the field of internet data processing technology. The method includes: an e-commerce storage server receiving a transaction request data packet signed by a transaction terminal using a user's private key; semantically parsing the transaction data and segmenting it into a sensitive privacy dataset and a public business dataset; constructing a two-branch Merkle tree structure, mapping the sensitive privacy data to the first branch after salting and hashing, and mapping the public business data to the second branch, generating a root hash value; packaging the root hash value, digital signature, and timestamp onto the blockchain for storage; and establishing an index to associate and store the data in a local encrypted database. This invention isolates privacy data and business data through a two-branch Merkle tree structure, and combines perceptual hashing and zero-knowledge proof technology to achieve integrity verification and tamper-proofing of transaction data while protecting user privacy.

The invention discloses a security assessment method for a multi-modal perceptual hash function, and belongs to the field of security of perceptual hash functions. The method comprises the following steps: firstly, judging a data format of multi-modal data; if the data is picture data, calculating a gradient by using an image processing algorithm; if the data are video data, using a frame-level processing mode; if the data is audio data, a gradient is calculated using an audio processing technique. Finally, the distance between the result and the target hash value is calculated, if the distance is smaller than a threshold value, it is considered that an input which is the same as the target hash value is successfully found, and therefore the perceptual hash function is not safe.

In a first aspect, the invention of the present disclosure comprises systems and methods for digitizing, authenticating, and analyzing paper-based manufacturing documentation. The invention integrates document scanning, optical character recognition, natural language processing, and machine learning to identify errors, omissions, and deviations in batch records. Cross-validation algorithms compare manual entries with electronically captured instrument data. Cryptographically verifiable barcodes ensure document authenticity. The system enhances traceability, accuracy, and regulatory compliance in biomanufacturing and other documentation-intensive industries. In a second aspect, a secure document authentication system assigns each page a cryptographically generated unique identifier encoded in a computer readable encoding, such as a 2D barcode, with embedded authentication tokens and perceptual hash data. The system verifies authenticity through post-execution scanning, hash comparison, and digitally signed certificates of authenticity.

The invention discloses a robust disaster detection method based on similarity and semantic information, and the method employs the following innovative scheme: introducing an inter-frame similarity calculation mechanism based on a perceptual hash algorithm (PHash), carrying out the real-time comparison of continuous frames in a monitoring video, and effectively perceiving whether a scene changes remarkably or not. The module can automatically filter static or redundant frames and screen out key frames possibly containing events, the logic process of a monitoring system in actual operation is met, the calculation load of follow-up processing is remarkably reduced, and the overall processing efficiency of the system is improved. By means of the pre-trained CLIP model, high-level semantic analysis of the monitored image is achieved, and preliminary anomaly judgment can be carried out on the image content from the perspective of vision-language combined representation. The module utilizes the strong cross-modal alignment capability of the CLIP to carry out robust evaluation on whether an image belongs to a normal scene or not, so that the adaptability of a system to an unknown scene and an unseen sample is effectively improved, and the overall recognition robustness is enhanced.

A non-contact weight measurement method based on two-dimensional pig contour image information, the method comprising the following steps: a, data acquisition: using a camera mounted above a pigpen to acquire two-dimensional images of the back of each pig, using an RTSP protocol to obtain a video stream from the camera, and using perceptual hashing technology to filter similar frames in the video stream, so as to obtain pig images and visual mark information on the back of each pig; using radio frequency identification technology to acquire pig serial numbers and recording same in a database; and, on the basis of the visual mark information on the back of each pig, acquiring serial numbers of pigs in the pig images; b, contour extraction: using a contour extraction network to acquire pig back contour information in the images; and c, weight measurement: on the basis of fine contour information, using a weight estimation network to predict the weight of each pig. The present invention enables real-time measurement of the weight of pigs in an actual production environment without restricting movement of the pigs, without requiring the pigs to maintain a specific posture, and without causing any stimulation to the pigs, thereby achieving high efficiency and accuracy.

The application provides a multi-dimensional construction data storage system and method, relates to the technical field of data storage, and realizes significant improvement of construction data storage efficiency and breakthrough of intelligent management capability through introduction of multi-dimensional feature extraction, resolution factor calculation and dimension perception hash algorithm. First, four-dimensional original characteristic values are extracted from each construction data unit, and standardized coding processing is carried out to generate a unified dimension code. Through calculation of time resolution factor, space resolution factor and coding similarity factor, the fineness and correlation strength of the data are quantitatively evaluated, so that the storage strategy is dynamically adjusted. The dimension perception hash algorithm intelligently generates logical storage coordinates by using these factors, so that data with similar characteristics naturally gather on physical storage. In the memory management layer, the data items are temporarily stored by maintaining an ordered structure, and are sorted according to the logical coordinates, so that the random disk write operation is effectively reduced, and the write performance is improved.

A kind of high-voltage disconnecting switch positioning and state identification method based on SURF feature matching, first through SURF feature matching method cooperate with the image similarity based on hamming distance measurement accurate positioning switch position, through perceptual hashing algorithm as similar determination measure accurately identify switch state.DCT is used to reduce frequency, higher than average hash algorithm and difference hash algorithm, to meet the requirements of high-voltage switch state identification in accuracy, and in the aspect of calculation speed, in the case of ensuring robustness and matching accuracy, SURF algorithm is faster than SIFT algorithm in speed, hamming distance calculation is faster than Euclidean distance calculation, and inherits the characteristics of good adaptability to the influence of actual environment such as illumination and angle, overcome the problems of existing high-voltage switch state identification method in sample size, calculation cost, accuracy, the positioning and identification of switch have higher accuracy.

The invention provides an operation decision-making method and device, and relates to the technical field of artificial intelligence, in particular to the technical field of intelligent agent decision-making cache, large models and cloud mobile phones. A specific embodiment of the method comprises the following steps: performing perceptual hash calculation on a user interface screenshot to generate an image perceptual hash value; performing hash calculation on the user intention text to generate a user intention hash value; performing hash calculation on the operation context to generate an operation context hash value; generating a composite cache key based on the image perception hash value, the user intention hash value and the operation context hash value; the cache is queried based on the composite cache key, in response to query success, an operation decision result corresponding to the composite cache key is obtained from the cache, and the cache correspondingly stores the reference composite cache key and the reference operation decision result.

The present application belongs to the technical field of electronic bidding and anti-encircling bidding, and aims to solve the problem that encircling bidding behavior is difficult to find and trace. The method of the present application comprises: generating a unique identification ID for the bidder and performing hash coding to generate a binary watermark sequence, processing the binary watermark sequence to obtain a redundant watermark sequence, encoding the information bits in the redundant watermark sequence into corresponding stripe units according to a preset gray coding rule, and embedding the stripe units at the bottom of the logo picture to form a hidden stripe watermark; calculating the perceptual hash value of the logo picture, and storing the perceptual hash value and the bidder identification information as watermark metadata in a database; extracting the logo picture from the PDF bidding file, determining the source of the bidding file and the identity of the bidder through perceptual hash fuzzy matching; based on the watermark verification result, constructing a bidding file watermark similarity matrix, and clustering and analyzing the abnormal correlation between different bidding files to determine whether there is encircling bidding behavior.

The invention relates to the technical field of image processing, solves the technical problem that an existing image mark point detection method is insufficient in recognition capability under the complex conditions of image large-angle inclination processing, coding area partial shielding and the like, and particularly relates to an image mark point detection method based on perceptual hash. A standard coding pattern with a feature structure is generated through a vector, and a perceptual hash value of the standard coding pattern is extracted for unique coding. At a detection end, after preprocessing operations such as graying, edge detection and perspective transformation are carried out on a real shot image, a coding area is extracted, a Hash value of the coding area is calculated, and finally pattern matching and recognition are carried out through Hamming distance comparison. The method can effectively improve the detection accuracy and robustness under the complex conditions of image inclination, occlusion, blurring and the like, has low calculation complexity, and is suitable for being deployed and used in various practical application scenes.

The application discloses an agent reinforcement learning reward function optimization method based on VLM closed-loop feedback. First, an initial reward function code and a parameter configuration file are generated according to natural language task instructions by using a large language model; then, the reward function is used to drive a robot arm to perform reinforcement learning strategy training, and a video stream of task execution is recorded and the task success rate is calculated. When the preset failure trigger condition is met, the parameter update is paused. An algorithm based on perceptual hashing and time sequence hybrid weight is used to extract a key frame sequence from the video, a visual language model is used for semantic diagnosis of the key frame, and improvement suggestion is fed back to the large language model, and the reward function is optimized and hot reloaded into the training process. By introducing the visual language model to construct an automatic feedback correction closed loop, the problem that the large language model generates a reward function lacking physical verification and leading to failure is solved, and the training efficiency and success rate of the agent for complex operation tasks are significantly improved.

The application discloses a kind of ocean environment's end cloud coordination data processing methods, comprising: the image of ocean environment is collected, and potential target-containing image is screened out;Potential target-containing image is positioned potential target area ROI and is carried out non-uniform semantic compression;According to confidence and position, the priority of compressed image is calculated and is arranged;The image after arrangement is calculated perceptual hash value, and is retrieved and compared in historical fingerprint library, and non-repeated image is transmitted to data lake;Non-repeated image is pre-labeled and the confidence of pre-labeled is evaluated, for the image of confidence higher than threshold value, directly output, for the image lower than threshold value, generate prompt word, according to the image lower than threshold value and its pre-labeled and prompt word, after artificial intervention, output.The application further discloses a kind of ocean environment's end cloud coordination data processing system.The method and system can effectively save bandwidth and improve annotation efficiency.

The invention discloses an image similarity detection method and device, and belongs to the technical field of image processing. The method comprises the following steps: firstly, quickly de-duplicating an original picture set by utilizing perceptual hash and a color histogram algorithm to obtain a candidate picture set; then, an improved OfficientViT network is adopted to extract image features, the network captures global context information and local details respectively through parallel self-attention branches and multi-scale local feature branches, and the global context information and the local details are spliced in a channel dimension; further, multi-level features in the network are integrated through a cross-layer feature fusion module, and the cross-layer feature fusion module performs weighted fusion on the features by using adaptive convolution and a channel attention mechanism; and finally, calculating cosine similarity based on the fused feature vectors, and judging whether the images are similar or not according to a preset threshold value. According to the invention, the accuracy and efficiency of image similarity detection are effectively improved.

The invention provides a perceptual hash method for image visual security evaluation. The method mainly comprises the following steps: obtaining a reference image and a perception encryption image, and carrying out bicubic interpolation preprocessing on the reference image and the perception encryption image; carrying out pixelation processing on the preprocessed image to generate horizontal and vertical complementary sub-bands; extracting a multi-scale low-frequency brightness feature, a texture feature and an image complexity feature from the direction complementary sub-band by using quaternion discrete wavelet transform; connecting the extracted features together to construct two hash sequences; and calculating a norm between the two hash sequences as a visual security score for perceiving the encrypted image. According to the method, the high efficiency of perceptual hash and the multi-dimensional feature extraction capability of quaternion discrete wavelet transform are combined, the accuracy, robustness and efficiency of visual safety assessment are remarkably improved, and meanwhile, the method better conforms to the perceptual characteristics of a human visual system.