902results about "Spoof detection" patented technology

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.

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.

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.

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.

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 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.

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.

Implementations are described herein for detecting deepfakes in digital media while preserving the privacy of the source computing device. In various implementations, sensor fingerprints and / or security tokens that signal software-introduced alterations, e.g., introduced by hardware abstraction layers (HALs) or virtual machines (VMs) may be utilized to detect such deepfakes. These signals may be used, separately and / or in combination, for various purposes, such as flagging digital content to a user as being a deepfake, preventing or blocking receipt and / or playback of digital content deemed to be a deepfake, allowing an end user to disable aspect(s) (e.g., layers) of digital content that are determined to be synthetic, etc.

The invention provides a human face living body detection method and device, electronic equipment and a medium, and the method comprises the steps: carrying out the modal image obtaining of a human face image, and obtaining an RGB modal image, an infrared modal image and a depth modal image of a human face; respectively carrying out feature processing on each modal image through a self-adaptive multi-modal Transform feature extractor to obtain key feature expression of the intensified modal image; performing selective enhancement on the key feature expression by using an expert enhancement projection module to obtain optimization features of each mode; performing cross-modal feature interaction on the optimization features among different modals by using the optimization features of the preset priority modals as guidance to obtain modal fusion features, and splicing the modal fusion features to obtain global fusion features; and judging whether the human face is a living body or not according to the global fusion feature to obtain a judgment result.

A method and system are disclosed for creating a graphic element representing encoded biometric data that represents the facial biometrics of two or more individuals. The method includes capturing facial images of each person, generating non-reversible face-derived feature vector data, and embedding this data into a graphic on the item. To validate that the graphic is derived from the people presenting the graphic, the graphic is processed to extract corresponding feature vector data, which is compared to the data derived from newly collected facial images of the presenting people. Validation indicators are based on threshold comparisons. The system supports verification using photographs on the item and enables secure association through trusted entities and cryptographic signatures. The graphic may encode alpha-numeric strings representing biometric and document data. The graphic may be applied to any document or item, including driver's licenses, passports, titles, ballots, and financial instruments.

The invention discloses a single image defogging method based on block-by-block nonlinear brightness prior, which belongs to the technical field of image processing, and comprises the following steps of: dividing a fog-containing image into local blocks, calculating the average brightness of the blocks, and constructing prior block-by-block monotone increasing nonlinear mapping to represent the corresponding relationship between the brightness of fog-containing blocks and the brightness of clear blocks; an atmospheric scattering model is combined, an atmospheric light vector is modeled into a vector, the vector and a PPWF form a parameterized recovery model, three scalar parameters are taken as a core, an optimal parameter is obtained through multi-target joint optimization, alternate optimization and golden section search, and finally a defogged image is generated. The method has the advantages of few parameters, low complexity and no need of training data, the definition and global contrast of far and near scenery can be remarkably improved while the image structure is maintained, and halo, excessive enhancement and color cast are effectively inhibited; the method has good robustness for different fog densities and illumination conditions, and is suitable for real-time and embedded defogging application of single-channel or multi-channel images.

The invention provides a face image detection method and device, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, in particular to the technical fields of computer vision, deep learning, large models and the like, and can be used for application scenes such as face living body detection and the like. According to the specific implementation scheme, a to-be-detected face image is acquired; inputting the to-be-detected face image into a pre-trained authentic identification prediction model, and generating an attention graph by the authentic identification prediction model; generating a detection cue word based on a preset cue word template; and inputting the to-be-detected face image, the attention map and the detection cue word into the visual language large model, so that the visual language large model generates output information including a detection result and a forged area coordinate.

The embodiment of the invention provides an identity verification method and device, equipment, a medium and a product, and relates to the field of biological recognition. The method comprises the following steps: acquiring multi-modal biological characteristic data of a user, and analyzing the multi-modal biological characteristic data through a living body detection technology; wherein the multi-modal biological characteristic data comprises a combination of at least two of the following biological characteristics: fingerprints, irises, voiceprints and facial images; according to the type of a service to be executed by the user, dynamically adjusting a priority strategy of biological feature verification; and generating an identity verification result based on the living body detection result and the priority strategy. According to the scheme of the invention, through combination of the multi-modal biological characteristic data and integration of a living body detection technology, diversity and anti-cheating capability of identity verification are enhanced, meanwhile, the dynamic priority strategy can be adaptively adjusted based on a service type, a verification process is optimized on the basis of improving verification safety, and thus reliability of identity verification is improved.

Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech ("deepfakes") in a call conversation. The server applies an NLP engine to transcribe call audio and analyze the text for anomalous patterns to detect synthetic speech. Additionally or alternatively, the server executes a voice "liveness" detection system for detecting machine speech, such as synthetic speech or replayed speech. The system performs phrase repetition detection, background change detection, and passive voice liveness detection in call audio signals to detect liveness of a speech utterance. An automated model update module allows the liveness detection model to adapt to new types of presentation attacks, based on the human provided feedback.

The invention relates to the technical field of image recognition, and discloses an image recognition method for cross-domain access authentication, which comprises the following steps: acquiring a to-be-authenticated image uploaded by an image acquisition node; converting the to-be-authenticated image to a frequency domain, and extracting a plurality of response signals in multiple preset directions and scales; calculating a phase consistency measure by using the real part and imaginary part response components, and generating a phase consistency feature map; carrying out statistics on local phase distribution of the phase consistency feature map, determining a phase distribution entropy representing physical attributes of an access medium, and carrying out weight reduction suppression on the phase consistency feature map according to the phase distribution entropy; according to the method, phase interference introduced by an unnatural display medium is recognized and suppressed, in-vivo detection is achieved in identity verification, and the influence of heterogeneous imaging amplitude distortion on recognition precision is eliminated.

The invention relates to the technical field of image processing, in particular to an image depth forgery detection method, system and equipment, and solves the problems of weak generalization, coarse positioning and no explanation of a traditional method by combining a large-scale visual language model (VLM), a guard context module and a segmentation progressive aggregation module. And multi-region accurate detection, mask positioning and explainable output are realized.

A system comprises a video capture module to acquire a video, a preprocessing module to enhance video quality and isolate regions of interest within the video, and a biometric data extraction module using remote photoplethysmography to extract heartbeat and SpO2 levels from the video. A machine learning module analyzes the extracted biometric data for liveness and deepfake detection, a verification module to compare the analyzed data against known biometric signatures, and a user interface to display analysis results and alerts.

The invention relates to the technical field of computer vision and biological feature recognition, and discloses a face anti-fraud method based on multi-modal feature fusion, comprising the following steps: acquiring face multi-modal image data of a user, the multi-modal image data comprising an RGB image, an infrared IR image and a depth Depth image; deep feature representation of the multi-modal image data is extracted through corresponding feature extraction backbone networks, and RGB modal features, IR modal features and Depth modal features are obtained; and inputting the multi-modal features into a dynamic weight sensing module. The face anti-fraud method based on multi-modal feature fusion aims to solve the problems that in an existing multi-modal face anti-fraud method, the modal fusion weight is fixed, sample specificity cannot be adapted, and discrimination information loss is caused by insufficient cross-modal feature alignment.

The invention provides a game anti-addiction face age evaluation method and device, and relates to the technical field of artificial intelligence, and the method comprises the steps: in response to a verification request of a game application, collecting a face image of a user through an image collection device of terminal equipment; inputting the face image into a lightweight age evaluation model deployed in the terminal device for reasoning, executing living body detection and age evaluation, and determining a living body detection result and age evaluation data; wherein the lightweight age evaluation model is constructed based on a lightweight convolutional neural network, and an age sensitive feature attention module is configured in the model and is used for dynamically improving an endowed weight for an age-related feature region in the face image; the living body detection process and the age evaluation process are carried out cooperatively; and generating a control instruction for the game application according to the living body detection result and the age evaluation data in combination with a preset local anti-addiction rule base.

A video detection model training method and apparatus, a video detection method and apparatus, and a device, relating to the technical field of artificial intelligence. The video detection model training method comprises: acquiring a video frame sequence, the video frame sequence comprising a plurality of video frames that are related to a target object and sorted in a chronological order (501); for m video frames in the video frame sequence, perturbing a key point of the target object in the m video frames to obtain a video frame sequence sample, there being an anomaly in the change of a feature associated with the key point of the target object in the plurality of video frames comprised in the video frame sequence sample (502); and training a video detection model on the basis of the video frame sequence sample (503). The present application perturbs key points to simulate phenomena "random jitter and drift of key points" present in fake videos so as to obtain samples, such that video detection models trained by means of the samples have a capability to detect "random jitter and drift of key points", thereby improving the detection accuracy of the video detection models.

A liveness detection model training method includes: blocking a part of a region in a sample image to obtain a masked image including a non-masked region, the non-masked region being an unblocked region in the masked image; obtaining an encoding feature of the non-masked region by performing feature extraction on the non-masked region by an encoder; obtaining an output image by performing feature restoration on the encoding feature of the non-masked region by a decoder; and updating model parameters of the encoder and the decoder according to a reconstruction loss value between the output image and the sample image.

The invention provides a palm vein high-security identity authentication system based on spectral analysis and deep learning, and relates to the technical field of biological recognition, the system comprises a hardware layer, a data processing layer, an algorithm layer and an application layer, through an antagonistic living body detection module in the algorithm layer, in combination with physical modeling and a GAN (Generative Adversarial Network), the high-security identity authentication of a complex imitation is realized. The method comprises the following steps: carrying out effective identification of a high-biomimetic material, carrying out physical modeling to analyze the absorption characteristics of palm veins in a hyperspectral range, such as the difference between oxyhemoglobin and oxyhemoglobin and periodic spectrum micro-interference caused by heart rate, and providing living body evidence based on physiological dynamics; meanwhile, a high-simulation template sample generated by the GAN is used for training a discriminator, the insight ability of the system to a template means is further enhanced, and compared with single traditional texture or static signal detection, the system combines physical characteristics with deep learning, and the confidence coefficient of in-vivo detection reaches up to 99.99%.

A method for electronic identity verification and management includes receiving a request for verification of identity for a customer from a service provider; requesting consent from the customer to access data from one or more data holders associated with the customer; receiving consent from the customer to access data from the one or more data holders and sending the consent to the one or more data holders; receiving customer data from the one or more data holders; processing the customer data to verify the identity of the customer, identifying one or more online identity sources of the customer and analysing the one or more online identity sources to determine a level of risk associated with the customer identity, and generating a verified compound digital identity for the customer based on the authenticated identity document and the one or more online identity sources of the customer without storing personally identifiable information.

A method for detecting video feed injection during an authentication session comprising initiating an authentication session which utilizes a video feed from a camera, the camera part of a computing device that is used to capture video of a user during the authentication session. Next, activating the camera to obtain a video feed, monitoring for the requested video feed, and receiving a video feed. Detecting a time delay between activating the camera and to receiving the video feed. Comparing the time delay to an expected delay or range of expected delay, and, if the time delay does not match an expected delay or range of expected delay, terminating the authentication session. Optionally providing a notification that the time delay did not match the expected delay, range of expected delay, or both. The number of times this method occurs may be repeated a random number of times.

The invention relates to the technical field of intelligent decision making, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses an intelligent shunting method, device, equipment and medium based on multi-modal information processing. And completing living body identity verification based on a three-dimensional structure and dynamic actions, identifying acoustic emotions and semantic emotions, fusing the acoustic emotions and the semantic emotions with interactive behaviors to generate a comprehensive emotional state, generating an access decision in combination with task complexity, executing an automatic service or a seat service, and providing a corresponding service response. According to the method, multi-modal information is formed by fusing vision, voice and interaction behaviors, user emotion and task complexity are obtained on the premise that living body safety verification is guaranteed, accurate access decision making is achieved, and therefore the intelligent level of service distribution and user experience are improved while identity verification safety and efficiency are improved.

The invention belongs to the technical field of image active defense, and provides a deep counterfeiting active defense method and system based on identity confusion, and the method comprises the steps: constructing a replacement identity encoder, and extracting the identity features of a face image; the method comprises the following steps of: introducing imperceptible antagonistic disturbance into a face image, and generating a protected image by taking identity characteristic deviation extracted by an identity encoder to be maximized and replaced as a disturbance optimization target, so as to ensure that the disturbance is imperceptible; and the identity feature process of the deep counterfeiting model is interfered by using the generated protected image, so that the image generated by the deep counterfeiting model deviates from the real identity of the user in the aspects of visual perception and identity features, and the deep counterfeiting attack is effectively defended. According to the invention, active identity confusion at a feature level is realized, and effective defense which does not depend on an attack model structure and has black box adaptability is realized.

The invention discloses a safety management and control method, system and equipment combined with biological characteristics and a medium, and the method comprises the steps: verifying the identity of a user in a manner of combining living body detection and behavior characteristic detection, and obtaining an identity verification result; calculating a trust score of the user in combination with a preset trust scoring model according to the identity verification result; dividing the users into N trust levels according to the trust scores, and configuring corresponding access permissions and security measures for each trust level; continuously monitoring physiological state change and operation behavior abnormity of the user in a user access process, and updating the trust score according to a monitoring result; and adjusting the trust level of the user through the updated trust score and a risk assessment algorithm. By constructing a zero-trust architecture based on multi-modal biological verification, the technical problems that a traditional power grid access control system is single in identity authentication and rigid in authority management are effectively solved.