8 results about "Anti spoofing" patented technology

A method for detection of a spoof call comprises: receiving a first voice response in response to a received call from a caller's device; augmenting the first voice response with a first watermark signal; sending the augmented first voice response to the caller's device; receiving a second voice response from the caller's device, wherein the second voice response is expected to contain therein an echo of the first watermark signal; analyzing the second voice response to determine the existence of the first watermark signal in the second voice response; and, generating a spoof call indication upon determination of the absence of the first watermark in the second voice response. System, circuit and non-transitory computer-readable medium embodiments are further provided.

Some disclosed methods involve providing a user prompt to place a user's digit in a fingerprint sensor area and obtaining, from a fingerprint sensor, fingerprint sensor data corresponding to the user's digit. Some disclosed methods involve determining, based on the fingerprint sensor data, that a currently-obtained fingerprint image matches one or more previously-obtained fingerprint images. Some disclosed methods involve extracting, from the fingerprint sensor data, N top-ranked anti-spoofing fingerprint feature types of a set of T anti-spoofing fingerprint feature types, N and T being integers and N being an integer smaller than T. Some disclosed methods involve performing an anti-spoofing process using the N top-ranked anti-spoofing fingerprint feature types.

The invention belongs to the technical field of artificial intelligence and computer vision, and particularly relates to a face anti-cheating detection method and system based on enhanced adapter fine tuning, and the method comprises the following steps: collecting a face image data set, carrying out the data preprocessing, and dividing the obtained data set into a source domain and a target domain; respectively training and verifying the ViT target detection network model of the integrated enhanced anti-spoofing adapter EAS-Adapter by using the source domain and the target domain to obtain a face anti-spoofing detection model; wherein the ViT target detection network model is that an EAS-Adapter is integrated on each Transform coding layer of a Transform coder in the ViT network, and the EAS-Adapter is integrated on each Transform coding layer of the Transform coder in the ViT network; and performing data preprocessing on a to-be-detected face image, and inputting the to-be-detected face image into the face anti-deception detection model to obtain a face authenticity identification result. According to the invention, each Transform encoding layer of the Transform encoder is integrated with the EAS-Adapter to capture complementary deception clues, and high-precision face authenticity identification in a cross-domain scene is realized.

The invention belongs to the technical field of network security, and discloses a user identification method and device, equipment, a storage medium and a product. According to the method, deception person prediction is carried out on all users through a preset deception person prediction identification model, deception users are obtained, then, the comprehensive scores of the camouflage persons corresponding to the camouflage persons are determined according to call information corresponding to the deception users, and the final scores of the camouflage persons are determined according to the comprehensive scores of the camouflage persons and the scores of an anti-deception center. And determining a risk population corresponding to the camouflage-prone person according to the final score of the camouflage-prone person. According to the method and the device, the deception user in the total amount of users can be obtained, then the comprehensive score of the camouflage person is determined according to the call information corresponding to the deception user, and the final score of the camouflage person is determined according to the comprehensive score of the camouflage person and the score of the anti-deception center, so that compared with an existing method of screening the camouflage person by an expert, the method and the device have the advantages that the workload is reduced; according to the method, the risk crowd corresponding to the camouflage-susceptible person can be determined according to the final score of the camouflage-susceptible person, so that the risk crowd susceptible to cheating can be automatically and accurately identified.

Aspects presented herein may enable a UE to verify the integrity of map data, thereby improving the accuracy and security of map-assisted positioning and / or map-based positioning. In an aspect, the UE performs map-assisted positioning based on a first set of map data. The UE verifies whether the integrity of the first set of map data satisfies an accuracy threshold. The UE discards the first set of map data if the verification of the integrity of the first set of map data does not meet the accuracy threshold. For example, the UE may compare a first set of map data to a second set of map data from a different source, and identify that the integrity of the first set of map data does not meet an accuracy threshold if the comparison shows an indication of an inconsistency above a consistency threshold.

The application provides a navigation anti-deception method, device and electronic equipment, wherein the method comprises the following steps: constructing a network topology structure according to a communication topology constraint and a space geometric relation of a formation network, wherein the formation network establishes time-frequency synchronization and unified coordinate conversion; selecting a plurality of nodes from the network topology structure to construct an anti-deception array; calculating distances between array elements in the anti-deception array and a signal source; comparing the calculated distances between the array elements and the signal source with actual height information of satellites to determine whether the received signal is an interference signal; constructing a positioning equation based on the anti-deception array and measurement parameters thereof, and solving the positioning equation by using a least square method to obtain position coordinates of the interference signal. The application can adapt to deception interference detection and interference source positioning under different terminal layouts and task requirements, and has lower cost and is efficient and reliable.

A network nodes pairing method having an anti-spoofing mechanism is provided used in a mesh network system. Each of node apparatuses in the mesh network system uses group information of the mesh network system as a key to encrypt network system identification information and self-identification information to broadcast a beacon signal including encrypted information. A scan process is executed by a child node apparatus to select a target father node. A validity verification process is performed on the beacon signal of the target father node according to the group information so as to determine the target father node passes the validity verification process when the target father node and the child node apparatus has the same network system identification information and when the self-identification information of the target father node is valid. A pairing request signal is transmitted to the target father node to request for pairing.

A method includes obtaining dual-pixel image data that represents an object and includes a first sub-image and a second sub-image, and generating (i) a first feature map based on the first sub-image and (ii) a second feature map based on the second sub-image. The method also includes generating a correlation volume by determining, for each respective offset of a plurality of offsets between the first feature map and the second feature map, pixel-wise similarities between (i) the first feature map and (ii) the second feature map offset from the first feature map by the respective offset. The method further includes determining, by an anti-spoofing model and based on the correlation volume, a spoofing value indicative of a likelihood that the object represented by the dual-pixel image data is being spoofed.