1537results about "Spoof detection" patented technology

The invention provides a method and system for preventing identity spoofing during digital authentication processes using artificial intelligence (AI)-driven pattern recognition. The system receives an input data stream from a user attempting to authenticate, which may include biometric data, device behavior data, or user interaction data. An AI-based pattern recognition model processes this data to analyze user behavior patterns and detect any anomalies that may indicate potential spoofing attempts. The system compares the processed data against a pre-established user profile to generate an authentication decision. If anomalies are detected, the system can flag the authentication for further review or trigger additional verification steps, such as multi-factor authentication (MFA) or one-time password (OTP) prompts. The system continuously learns from user interaction data and dynamically updates the user profile to improve the accuracy of identity verification.

A method is provided for improving image classification accuracy in few-shot learning scenarios, where only a limited number of training examples are available. The method combines the use of Gabor filters and convolutional neural networks (CNNs) to extract detailed texture and orientation features from images. These features are then enhanced through global average pooling, aggregated into comprehensive feature vectors, and refined using an attention mechanism that identifies and emphasizes the most relevant features for classification. Masks generated from this attention process selectively enhance critical features, which, after optional re-encoding, are used to train a classifier via a metric learning approach. This method aims to increase feature separability and classification performance, facilitating more accurate classification of new images with minimal training data.

Systems, methods, and computer program products are disclosed for selectively employing a processing mode based on a continuous gesture. Selectively employing a processing mode may include detecting an existence of a mode-selection gesture by an individual. Upon detecting the existence of the mode-selection gesture, a first mode for processing facial micromovements of the individual may be continuously implemented while continuously detecting the mode-selection gesture. Following the continuous detection of the mode-selection gesture, a cessation of the mode-selection gesture may be detected. In response, implementation of the first mode may cease, and implementation of a second mode different from the first mode may be initiated for processing facial micromovements of the individual.

The invention discloses a power plant specific area personnel behavior identification and early warning system, and relates to the technical field of industrial safety intelligent monitoring, the power plant specific area personnel behavior identification and early warning system comprises a data acquisition module, an edge calculation module and a central processing platform, the edge computing module carries out lightweight processing and localized decision making on original data, the load of the central processing platform is reduced, and the central processing platform realizes full-process safety management and control of a high-risk area through multi-modal data fusion, dynamic authority management and trajectory analysis. According to the invention, lightweight AI models are embedded in various cameras of a work card authentication terminal and a visual perception unit, preliminary reasoning is directly completed at the work card authentication terminal and the cameras, so that the work card authentication terminal and the cameras have edge computing capability, and independent edge servers or industrial personal computers are deployed near data sources of various regions of a power plant. And serving as a regional edge computing node.

Systems, methods, and computer program products are disclosed for establishing nonvocalized communications. Establishing nonvocalized communications may include detecting a directional gesture made by a wearer of a first wearable device configured to determine facial skin micromovements of the wearer, wherein the directional gesture identifies a second device in proximity to the first wearable device. Based on the directional gesture, a wireless communication channel for enabling a nonvocalized communication between the first wearable device and the second device is selected. Thereafter, specific facial skin micromovements of the wearer of the first wearable device are determined. The specific facial skin micromovements indicate words to be communicated in an absence of perceptible vocalization by the wearer of the first wearable device. Then, the words are transmitted via the wireless communication channel from the first wearable device to the second device for presentation via the second device.

The invention discloses a deep counterfeiting detection model training method, a deep counterfeiting detection method and a deep counterfeiting detection system, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a training data set containing a real image and a plurality of counterfeit images, and enabling the image to be provided with a label for representing the authenticity; in the training process, the deep forgery detection model can be in contact with various types of image samples, so that wider and more complex image features and forgery modes can be learned, a forgery reason is further marked for a forgery image, and the deep forgery detection model can be helped to deeply understand essential features of forgery content in the training process. Therefore, the problem of insufficient detection capability for well-designed and high-quality counterfeited contents can be solved, and the technical effect of improving the accuracy of counterfeited detection is achieved.

The invention discloses a power transmission line constructor identity verification method based on face recognition, and relates to the technical field of electric power engineering safety management, and the method comprises the steps: collecting multispectral face data, and generating a spectral stereo feature matrix; micro blood flow pulsation characteristics, skin texture characteristics and thermal imaging temperature distribution characteristics are extracted from the spectrum stereo characteristic matrix, a three-layer cascade anti-counterfeiting verification mechanism is constructed, and comprehensive living body judgment is carried out; establishing a distributed feature matching network, and matching the spectrum stereo feature matrix with a pre-stored feature library; collecting a geographic position track and an operation behavior mode of a constructor, and carrying out multi-dimensional cross validation on the geographic position track and the operation behavior mode and an identity matching result to generate a multi-level safety evaluation index; and writing the identity verification process and the multi-level security evaluation index into a distributed account book, and generating a verification voucher. According to the invention, cross analysis is carried out on the identity matching result of the constructor and the behavior characteristics, so that the problem that a traditional face recognition system is easily falsely used by the identity is effectively solved.

The invention relates to the technical field of computer vision and pattern recognition, in particular to a deep-forged face image detection method based on a double-flow CNN and ViT hybrid model. The method comprises the following steps: preprocessing a real video and a forged video to obtain an original face image; obtaining a high-frequency noise residual error feature image of the original face image based on an improved high-frequency noise residual error feature extraction module; performing first feature enhancement operation on the original face image and the high-frequency noise residual feature image based on a double-flow CNN; carrying out secondary feature enhancement operation on the original face image and the high-frequency noise residual feature image which are subjected to the primary feature enhancement operation on the basis of double-flow ViT; and fusing the features of the original face image and the high-frequency noise residual feature image after the secondary feature enhancement operation, performing true and false prediction on the fused features, and outputting a detection result. The objective of the invention is to solve the technical problems of feature splitting, local-global imbalance and insufficient cross-modal interaction in the prior art.

The invention belongs to an image processing technology, and particularly relates to a deep forgery detection method and system based on face embedded difference guidance, and the method comprises the steps: generating a forgery image according to an input real image, and calculating a difference image and a semantic difference vector of two images; learning the features of the real image and the forged image by using a first classifier, and learning the features of the real image and the difference image by using a second classifier; splicing and fusing the semantic difference vector and image features extracted from the difference image through a channel to obtain fused features, and learning the difference between the real face features and the fused features by using a third classifier; and calculating the distillation loss between the fusion feature and the image feature of the forged image, and training in combination with the loss of each classifier. According to the method, multi-level feature fusion, a three-classifier architecture and a knowledge distillation mechanism are adopted, the strong generalization ability across data sets is achieved, and the system can output a high-precision deep forgery judgment result.

According to various embodiments, a system and method for verifying a user's identity using both document-based and biometric data is disclosed. The system may prompt a user to upload an image of a government-issued identification document and extract user information from the image using optical character recognition (OCR). The system may also extract the embedded face image from the ID and prompt the user to take a real-time selfie while performing one or more randomized actions or poses. A facial recognition engine may compare the extracted ID image to the live selfie to determine a similarity score. Based on this match, along with optional document authenticity checks, the system may confirm the user's identity in real-time for use cases such as account access, onboarding, and regulatory KYC compliance.

The invention discloses a collaborative anti-interference access control system based on face recognition, and belongs to the technical field of access control systems. The cooperative anti-interference access control system comprises a multi-mode sensing acquisition module, an image preprocessing and anti-interference module, a face recognition and living body verification module, a cooperative control and decision module, a remote management and iteration module and an access control execution module. According to the access control system, the working mode can be adjusted in real time according to environmental changes, the influence of various interference factors is effectively resisted, a more stable and reliable face recognition access control service is provided for a user, the complementarity of multi-source data can be fully utilized by the access control system, the collected target image is optimized and processed, and the user experience is improved. The influence of interference factors such as light and noise on the image is reduced, the reliability and the accuracy of data are effectively improved, and the anti-interference capability and the face recognition accuracy of the system in a complex environment are enhanced.

The invention discloses a deeply-forged face video frame-level positioning method and system based on weak supervised learning, and the method comprises the steps: firstly constructing a training set with a video as a unit, carrying out the data enhancement of a frame-level sample, and generating an enhanced view pair; secondly, splicing the enhanced view pair and inputting the spliced enhanced view pair into a depth forgery detection model to obtain and generate fusion enhanced frame-level features; then, intra-class contrast learning loss, time sequence consistency constraint loss and frame weight loss are constructed, and a deep forgery detection model is trained based on fusion-enhanced frame-level feature joint optimization. And finally, inputting a video to be detected into the deep counterfeiting detection model to output the frame-level confidence, judging whether the video is a forged video or not, and realizing frame-level counterfeiting positioning. According to the method, video-level detection and frame-level positioning are effectively realized, meanwhile, the influence of label noise in part of forged videos is relieved, and the generalization and robustness of the system are improved.

A private identity system performs on-device embedding and homomorphic tokenization (HT) to map plaintext inputs to non-invertible HT tokens. During enrollment, HT tokens are stored and a centroid can be computed for each user. During prediction, a new HT token shortlists nearest centroids, followed by 1:1 distance verification against stored embeddings. On success, the system returns a UUID bound to the user. Because passkeys can be shared via device or account keychains, the UUID binds the ceremony to the same enrolled individual without exposing biometrics.

The invention discloses an incremental counterfeit face image identification method based on cross-domain feature alignment, which belongs to the field of deep counterfeit detection, and comprises the following steps of: constructing a face image counterfeit detection model, extracting a network by taking Xception as a main feature when the model is constructed, a task adaptive weight correction module, a class awareness supervision comparison learning module, a double knowledge distillation module and a classification discrimination module are synchronously introduced; defining a task sequence, and constructing a task sample set and a task test set for subsequent model training and testing; a domain chain progressive training mechanism is adopted to train the face image forgery detection model, and the training process is composed of a basic stage and a plurality of incremental task stages; after training of each task is finished, testing is carried out; and obtaining a current to-be-identified face image, and inputting the final face image counterfeiting detection model to obtain an identification result. According to the method, the accuracy and generalization of face image counterfeiting detection can be effectively improved.

The invention discloses a deep forgery detection method based on a multi-granularity collaborative attention mechanism, and the method comprises the following steps: extracting multi-level features through a backbone network, achieving the self-adaptive region division and weight fusion through a dynamic space grouping attention mechanism, and solving the problem that a conventional method is insufficient in attention to a distributed forgery region; a double-path channel decoupling module is designed to separate high-frequency artifacts and low-frequency semantic features, and feature coupling interference is eliminated; a cross-granularity feature interaction network is constructed, local details and global semantic features are collaboratively optimized, and the fusion efficiency of multi-scale forged clues is improved; and finally, a detection result is output through the classifier. According to the method, the defects of weak cross-domain generalization ability, low multi-granularity information utilization efficiency and the like caused by incomplete space attention coverage and channel feature interference in the prior art are overcome, the detection precision and robustness are remarkably improved, the stable performance is kept in a complex degradation scene, and the method can be widely applied to scenes such as digital content security auditing and identity authentication.

Systems, methods, and non-transitory computer readable media including instructions for providing private answers to silent questions are described. Providing private answers to silent questions includes receiving signals indicative of particular facial micromovements in an absence of perceptible vocalization; accessing a data structure correlating facial micromovements with words; using the received signals to perform a lookup in the data structure of particular words associated with the particular facial micromovements; determining a query from the particular words; accessing at least one data structure to perform a look up for an answer to the query; and generating a discreet output that includes the answer to the query.

The invention discloses a face forgery detection method and system based on multi-view collaborative fusion, and relates to the technical field of computer vision, and the method comprises the steps: obtaining to-be-detected video frame data; preprocessing the video frame data to be detected to obtain a standardized input image tensor; and inputting the standardized input image tensor into a pre-trained face counterfeiting detection model, and processing the standardized input image tensor by the face counterfeiting detection model to generate a face counterfeiting detection result. According to the method, the technical problem of poor detection performance of the model in cross-library and complex environments is solved, the detection robustness of the model in complex scenes such as fuzzy and compressed scenes is remarkably improved, and the detection precision is remarkably improved.

The invention discloses an unsupervised deeply-forged face video detection method based on strong enhancement contrast learning, which comprises the following steps: firstly, preprocessing face video data, cutting out a full-size label-free image and a face region image, then preliminarily classifying the label-free data and endowing the label-free data with pseudo labels by using natural physiological characteristics in face features; a strong enhancement contrast learning strategy is adopted, a plurality of basic enhancement operations are randomly combined to generate a strong enhancement view with larger structure change and semantic difference, strong enhancement contrast learning of the face image is completed, and finally accurate detection of a counterfeit face video is realized through diclustering and authenticity discrimination. Compared with an existing unsupervised detection method, the method has the advantages that multi-level feature fusion and strong enhancement contrast learning are combined, deep counterfeit traces can be captured more effectively, higher robustness and generalization ability are shown in complex scenes such as noise and compression, and the method is high in robustness and generalization performance. And high-precision detection of the deeply-forged face video under an unsupervised condition is realized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology, leasing and the like, and discloses an operation authenticity verification method, which comprises the following steps: executing operator identity verification, and generating a verification record; starting a countdown mechanism, and monitoring terminal position information; when the initial verification distance accords with a first preset distance threshold value, generating a job permission instruction and receiving task data; acquiring real-time position information of the terminal, and determining a submission verification distance; when the submitted verification distance accords with a second preset distance threshold value, a verification passing timestamp is extracted, and a time effectiveness state is generated; and when the state is valid, storing the task data and the space-time verification information. According to the invention, identity verification and position verification are combined to ensure that an operator executes a task at a target location; the countdown mechanism limits the operable time after identity verification, so that the equipment is prevented from being transferred to others for use; the timeliness verification mechanism ensures that the data submission time is reasonable, and the authenticity and safety of task execution are improved.

The invention provides a video depth forgery detection method based on space-time inconsistency and frequency domain analysis, the forgery detection method constructs a video depth forgery detection network of an unknown environment image, and the video depth forgery detection network comprises a space-time inconsistency module, a frequency domain analysis module and a video level classifier. The space-time inconsistency module captures space-time features in video frames, through a double-flow cooperation mechanism integrating space-time inconsistency modeling and frequency domain artifact analysis, the space-time features in videos are efficiently captured, dynamic information interaction between key frames is achieved, and therefore an enough feature basis is provided for deep counterfeiting detection. And the frequency domain analysis module combines a deep neural network with time-frequency analysis and maps video frames to a frequency domain so as to realize end-to-end identification of an unknown tampering mode and provide support for understanding of fine-grained content and improvement of classification precision.

The embodiment of the invention provides a counterfeit recognition method and device based on local image understanding, and the method comprises the steps: receiving a to-be-recognized image, extracting the features of a counterfeit region of the to-be-recognized image, generating an abnormal region thermodynamic diagram of a suspicious counterfeit region corresponding to the to-be-recognized image based on the features of the counterfeit region, and carrying out the recognition of the abnormal region thermodynamic diagram. Under the condition that the spatial distribution of the abnormal region thermodynamic diagram is reasonable, generating structured text prompt information according to a preset prompt information format based on the abnormal region thermodynamic diagram and the to-be-recognized image, and encoding the text prompt information and the to-be-recognized image to obtain text embedding features, and activating a target expert model corresponding to the suspicious counterfeit region based on the text embedding feature, and processing the corresponding suspicious counterfeit region through the target expert model to obtain a counterfeit confidence coefficient and a counterfeit recognition result corresponding to each suspicious counterfeit region. According to the method, the defect that local counterfeit information is easy to omit is effectively overcome, and the accuracy of counterfeit identification is remarkably improved.

User authentication, spoofing and replay attack prevention, liveness detection, and user-and-document verification using a live video stream with spatial challenges. A camera of an electronic device captures and transmit a live selfie user-facing video, as part of a user registration process. The user is instructed to spatially move his body or face, such that his face would appear within a first particular on-screen shape; and to also, concurrently or simultaneously, spatially hold in his hand or move a particular an identification document such that it would appear within a second on-screen shape. Optionally, the on-screen shape moves on the screen, and the user is required to spatially move the relevant item to keep it within the boundaries of the moving on-screen shape. The system then analyzes the video via computerized vision, to determine whether the user complied with the spatial manipulation challenges.

Embodiments of the present application provide a fingerprint anti-counterfeiting neural network training method, a fingerprint anti-counterfeiting method and device, comprising: obtaining multiple sets of training data, each set of training data including: first native domain data, second native domain data and third native domain data; using the multiple sets of training data to train an initial classification network to obtain a target classification network, the initial classification network including a fusion subnetwork and a classification subnetwork, for each set of training data, the fusion subnetwork is used to generate a first fingerprint matching pair based on the feature description matrix of the first native domain data and the second native domain data, and generate a second fingerprint matching pair based on the feature description matrix of the first native domain data and the third native domain data, and the classification subnetwork is used for fingerprint classification and identification based on the first fingerprint matching pair and the second fingerprint matching pair. Through the present application, the problem of low identification accuracy of true and false fingerprints in related technologies is solved, and the effect of improving the identification accuracy of true and false fingerprints is achieved.

A method of maintaining the security of an authentication session comprises operating a camera device to continuously capture a view of an environment of a computing device, performing face detection to detect all faces in the environment, performing comparisons between faces detected in the environment and faces of a set of authorized users, at a first time, detecting a first face of an authorized user of the set in the view of the environment and, in response, allowing the authorized user to operate the computing device, and at a second time after the first time, one of failing to detect the first face or detecting a second face that is not of an authorized user of the set in the view of the environment, and, in response, at least partially blocking operator access to input and output devices including blocking a display of the computing device.

The invention discloses a deep forgery detection method and system based on frequency domain channel selection and mutual information, and belongs to the technical field of image processing. The method comprises the steps that a face image data set is acquired and preprocessed; constructing a deep forgery detection model and performing training by using the preprocessed face image to obtain a trained deep forgery detection model; and obtaining a face image to be subjected to forgery detection, and obtaining the authenticity of the face image to be subjected to forgery detection by using the trained deep forgery detection model. The method not only improves the detection accuracy of multi-type counterfeit techniques, but also has strong compression resistance and cross-dataset generalization ability, and is suitable for security detection tasks of various complex and multi-source counterfeit contents.

A method for enhancing detection of morphed biometric modality data is provided that includes receiving, by an electronic device, biometric modality data of a person, extracting feature vectors from the biometric modality data, normalizing the feature vectors, encoding the normalized feature vectors into qubits, and expanding, using at least one quantum algorithm, the normalized feature vectors into a high-dimensional space. Moreover, the method includes generating a distribution from the high-dimensional space based on the qubits, calculating a deviation between the generated distribution and a corresponding record high-dimensionality feature vector distribution of the person, and comparing the calculated deviation against a threshold deviation value. In response to determining the deviation satisfies the threshold deviation value, the method determines the received biometric modality data was morphed.

The embodiment of the invention provides a face image anti-counterfeiting detection method and device based on frequency spectrum reconstruction, and the method and device achieve the effective utilization of frequency domain information through the innovative design of a frequency spectrum reconstruction system and the shielding reconstruction and feature extraction. And an anti-counterfeiting classification mechanism is constructed, and a reliable detection model is established in combination with reconstruction features and threshold discrimination. And score calibration is introduced, and the detection accuracy is ensured through parameter fitting and statistical analysis. According to the method, the defects of the traditional technology in the aspects of spectrum processing, anti-counterfeiting classification, score calibration and the like are effectively overcome, and a technical guarantee is provided for face anti-counterfeiting.

A method for detecting video feed injection or camera bypass attacks during a computing device based authentication session, utilizing a camera's video feed. Activating the camera to request a video feed having designated camera settings and receiving a video feed, which may be a video injection of the actual video feed from the camera. Detecting a time delay between activating the camera and receiving a video feed and comparing the time delay to an expected delay. Camera settings are compared to the characteristics of the received image feed. If the time delay doesn't match an expected delay or range of expected delays, terminating the authentication session. If the video feed characteristics don't match expected video feed characteristics, based on the camera settings, terminating the authentication session. The camera security cycle may be repeated a random number of times during a security session and the camera settings may be randomly selected.

Object description information that is transmitted by a biometric recognition apparatus is received, the object description information includes a biometric feature of a target object and location information of the target object. Identity information of the target object is obtained according to the biometric feature. A service information set in association with the identity information is obtained. From the service information set, one or more pieces of candidate service information are selected. The one or more pieces of candidate service information are transmitted to a terminal device associated with the identity information. At least a first piece of target service information returned by the terminal device is received. At least a first service corresponding to the first piece of target service information is processed. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

The invention discloses a forged face image generation method and recognition method based on a diffusion model, and the method comprises the steps: collecting various watermark signatures used for a forged face image and a plurality of vanilla images, training an encoder and a decoder of a HiDDeN model, and employing the trained decoder as a watermark extractor to carry out the detection of a watermark in the forged face image; a plurality of vanilla images are collected to form a data set, an original watermark signature m is distributed to each training sample in the data set so as to train an LDM network model, a watermark extractor is adopted to carry out watermark extraction on output images of the LDM network model, and a watermark signature # imgabs0 # is obtained; and constructing a total loss function by using the watermark signature # imgabs1 #, the corresponding original watermark signature m and the image perception loss, and finally acting the total loss function on a back propagation process of a decoder of the LDM network model, so that the LDM network model learns the capability of generating the watermark image.