49 results about "Perceptual hashing" patented technology

The invention provides a distributed multi-source digital content tamper-proof Hash evidence storage method based on a block chain, and relates to the technical field of content tamper-proof, and the method comprises the steps: standardizing multi-source digital content, constructing an evolution pedigree tree, implanting a detector to capture change features, and tracking a propagation path; constructing a dynamic perceptual hash network to generate a dynamic hash value containing associated information; and verifying and generating an evidence storage record through a progressive consensus mechanism. According to the invention, efficient tamper-proof evidence storage of the multi-source digital content is realized, and the data traceability and verification efficiency are improved.

The invention discloses an edge AIGC environment-oriented verifiable ownership storage method and framework. The method comprises the following steps: constructing a perceptual hash and verifiable storage ownership framework comprising a user, an edge AIGC generation server, an ownership evidence calculation node and a distributed evidence warehouse; a user submits an AIGC generation request to the edge AIGC generation server, and the edge AIGC generation server generates a target AIGC image; performing perceptual feature extraction on the target AIGC image to generate a unique hash sequence; binding the unique hash sequence with a user ID and sending the unique hash sequence and the user ID to the distributed evidence warehouse, and after the user confirms that the target AIGC image is received, storing binding information of the unique hash sequence and the user ID by the distributed evidence warehouse; and when a suspected infringement image appears, calculating a hash sequence of the suspected infringement image and querying an originally stored unique hash sequence to judge and determine whether infringement occurs, so as to realize verifiable storage of the ownership of the AIGC image.

The invention relates to the field of hybrid coding of video engines, and provides a hybrid coding processing method, system and equipment based on a video engine, which comprises the step of intelligently switching an H.265 inter-frame predictive coding mode and an MJPEG (Multijoint Joint Photographic Experts Group) intra-frame compression coding mode by dynamically monitoring the proportion of a motion area in a video picture. When a scene is static, discrete cosine transform is adopted to compress space redundancy, motion vector compensation is started to eliminate time redundancy when motion is violent, and meanwhile, key frames are doubly screened by using pixel difference analysis and a perceptual hash algorithm, and repeated frames of visual redundancy are eliminated. And finally, space-time association is established for the optimized double-code-stream data through a timestamp index system, and efficient storage and accurate reconstruction of the mixed code stream are realized. The problems that the coding efficiency is low, no self-adaptive coding mode exists, redundant frames are not optimized, and code stream management is insufficient are solved.

The invention discloses a medical image robust watermarking algorithm based on a re-parameterized high-efficiency attention network, and relates to the field of image data processing methods, and the algorithm comprises the steps: obtaining an original medical image; a pre-trained RepEA-Net model is used for extracting multi-scale deep features with good generalization performance from an original medical image, and then a mean perceptual hash algorithm is used for generating feature vectors. Obtaining an original watermark image; the watermark image is encrypted by using a Logistic chaotic encryption algorithm, the security of the watermark image is improved, and finally, the encrypted watermark image and the binary feature vector are subjected to XOR operation to generate the zero watermark. Through the innovative design of the re-parameterization efficient attention network, the contradiction among robustness, generalization and image integrity of a traditional medical image watermarking algorithm is broken through, and a solution with technical advancement and clinical practicability is provided for copyright protection and safe transmission of medical images in the medical Internet of Things.

The invention discloses a video content protection method and device based on a multi-channel image feature security mark, and relates to the technical field of video content security management, and the method comprises the steps: obtaining an original video, and checking the original video; acquiring a video frame image of the audited video, and performing fusion, dimension reduction, compression and encryption processing on multichannel high-order features of the video frame image through a perceptual hash algorithm, an interpolation hash algorithm, a compression coding principle and a national cipher SM4 algorithm to generate a dynamic security mark; embedding the dynamic security mark into the video frame image to generate an approved video; and extracting the dynamic security mark of the currently played video, and checking the consistency between the feature information extracted from the dynamic security mark and the feature information of the played video to judge whether the video is compliant or not. The video content is protected, the security of the video is improved, the compliance check of the video content is realized, and the accuracy and efficiency of compliance judgment of the video content are improved.

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 belongs to the technical field of copyright protection, discloses a lightweight active defense picture copyright protection method, and aims to solve the problems of penalty after infringement, huge storage overhead caused by full-node storage and undifferentiated copyright and use right in copyright protection. The invention provides a lightweight active defense picture copyright protection scheme. On one hand, the scheme is based on the perceptual hash technology, active defense is achieved, and malicious resale behaviors are completely eradicated before infringement behaviors occur; on the other hand, storage optimization is realized based on a chameleon hash algorithm, and transaction information of the same copyright is added to an original block under the condition that a block head is not changed; meanwhile, a copyright chain and a use right chain are designed, the copyright can be transacted, and the use right is only allowed to be downloaded and checked after identity verification.

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.

Methods and systems for efficiently generating and verifying 3D perceptual hashes of 3D models are provided. Embodiments include providing a 3D model of an object, the 3D model including a plurality of mesh vertices; finding a minimal bounding sphere substantially enclosing the plurality of mesh vertices and having a center corresponding to the center of mass of the object; calculating, based on the minimal bounding sphere, one or more sub-spheres concentric with the minimal bounding sphere and evenly distributed inside the minimal bounding sphere. Further, for each sphere: projecting one or more normal vectors from the center of the sphere to one or more object faces intersecting the sphere; calculating, based on the one or more normal vectors, one or more first distances from the center of the sphere to the one or more object faces; extending the one or more normal vectors up to a radius of the sphere to project the one or more normal vectors as one or more dome points on the sphere; calculating one or more second distances between the one or more dome points; and then mapping the one or more first and second distances corresponding to all spheres to a hash space to generate a 3D perceptual hash value of the 3D model.

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 invention discloses a picture interception method and device, equipment and a storage medium, and belongs to the field of application software. The method comprises the following steps: calculating perceptual hash of a to-be-uploaded picture, and obtaining hash fragments of the perceptual hash; according to the Hash fragment of the to-be-uploaded picture, matching from the at least two Hash buckets to obtain a target Hash bucket; the hash bucket corresponds to a bucket hash value, the hash bucket is used for storing a reference picture, and a hash segment of the reference picture is the same as the bucket hash value of the hash bucket; the target bucket hash value of the target hash bucket is the same as the hash segment of the to-be-uploaded picture; under the condition that a reference picture similar to the to-be-uploaded picture exists in the target hash bucket, carrying out steganography content analysis on the to-be-uploaded picture; and intercepting the to-be-uploaded picture under the condition that the steganography content exists in the to-be-uploaded picture. According to the method, the picture with the steganographic content can be intercepted quickly and efficiently.

The invention discloses a similar image search method, system and device based on perceptual hash and a medium thereof, and the method comprises the steps: constructing a multi-scale space for an input initial image, generating a Gaussian pyramid, and obtaining a Gaussian difference pyramid based on the Gaussian pyramid; screening stable key points in the Gaussian difference pyramid; according to the distribution density of the screened stable key points, delimiting a densest area in the initial image, and intercepting a minimum bounding rectangle as a target screenshot image; extracting multi-modal features of the initial image, and performing feature splicing to generate a joint feature vector; preprocessing the initial image and the target screenshot image; generating a corresponding Hash character string based on the preprocessed image and the joint feature vector; and comparing the Hash character string of the image to be retrieved with the Hash set in the database, and counting the image of which the Hamming distance is smaller than or equal to a preset threshold value as a similar result to be output. According to the method, the search accuracy of the screenshot image is improved, so that complete original image information is obtained.

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 present invention relates to the field of information retrieval technology, and more particularly to an image retrieval method, device, and storage medium based on deep difficulty-aware hashing. In the present invention, adaptive difficulty-aware enhancement is performed on original data samples to comprehensively model the global geometric structure of the embedding space, dynamically adjusting the difficulty level by linear interpolation of the embedding. Difficulty-aware feature synthesis is also performed to generate semantically consistent and label-preserving enhanced data samples for cyclic training. This strategy not only fully utilizes the potential information in all training samples, but also systematically challenges the learned metric with an adaptively calibrated difficulty, ensuring continuous optimization across the entire feature manifold and promoting the generation of similarity-preserving hash codes.

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 relates to the technical field of image detection, in particular to a work meeting picture uploading control method and system based on image similarity detection. According to the job meeting picture uploading control method based on image similarity detection, legality verification and temporary storage are carried out on an uploaded picture, intelligent blocking is carried out, and an MD5 hash value and a perceptual hash value are calculated in parallel; and constructing a three-level detection mechanism, and if the picture file is a new picture or a user selects to upload the picture file, starting an upload process of a lightweight distributed file system FastDFS, and writing the picture file into a disk. According to the work meeting picture uploading control method and system based on image similarity detection, repeated pictures and similar pictures can be effectively recognized, redundant storage is reduced, the storage efficiency is improved, the manual workload is reduced, the retrieval efficiency is improved, and the picture management requirements of work meeting activity scenes can be flexibly met.

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 present invention belongs to the field of multi-label spatial dataset sample search and discloses a hash-based similarity measurement method for multi-label spatial datasets. The method includes data preprocessing: mapping the coordinates of points in the spatial dataset to plane coordinates within the outer bounding box, and calculating the density of data points with each label within each grid to obtain a density matrix; quasi-multi-channel image generation: mapping the density values ​​to "color channel values" under the corresponding channels and calculating the composite channel values; hash string generation: using mean hashing, difference hashing, and perceptual hashing methods to generate hash strings for each dataset to obtain hash string tuples; and similarity calculation: calculating the Hamming distance between hash strings to obtain the similarity between multi-label spatial datasets. The present invention balances algorithm accuracy and efficiency, maintains low time complexity, and accurately and quickly measures the similarity between multiple labeled spatial datasets.

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.

The invention discloses a credit material repeated detection method and equipment based on image fingerprints. The method comprises the following steps: receiving an original image of a credit material uploaded by a user, and preprocessing the original image; extracting image fingerprint data from the preprocessed image, wherein the image fingerprint data comprises a perceptual hash fingerprint and a deep semantic vector; performing fingerprint matching on the original image and a historical image based on the image fingerprint data of the original image and the fingerprint data of the historical image in a historical database, and if the fingerprint matching is successful, determining that the original image of the credit material uploaded by the user is a repeated image; generating a repeated image judgment result in combination with the user identity and the time sequence; and outputting a structured detection result based on the structured interpretable output mechanism. According to the method, through joint modeling of the perceptual hash fingerprints and the deep semantic vectors, the problem that a traditional image deduplication method can only identify identical files is effectively solved, and the accuracy and sensitivity of identifying forged materials are improved.

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.

This invention discloses an attribute-aware hash coding method for large-scale fine-grained image retrieval, comprising the following steps: extracting multi-scale image features using a convolutional neural network and fusing features at different scales using an upsampling strategy; using a feature mining module consisting of a self-attention mechanism and a feedforward network to mine significantly discriminative features in the image; interacting with the extracted features using a set of learnable queries to directly decouple a set of specific visual attribute features that can distinguish different categories of images; each query-captured specific visual attribute feature is compressed as a one-bit hash code; furthermore, an additional auxiliary branch is used during training to effectively alleviate the optimization difficulties in low-bit hash coding scenarios, enabling better optimization of learnable queries and improving the retrieval performance of hash codes. This invention effectively captures significantly discriminative visual attribute features from complex global image features by setting a set of learnable queries, ensuring that the final generated hash code retains the key semantic information of the image and achieves higher image retrieval accuracy.

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.