公开(公告)号：US20240152596A1

公开(公告)日：2024-05-09

申请号：US18231462

申请日：2023-08-08

Applicant: iProov Limited

Inventor： Panagiotis Papadopoulos ,  Bruno Voigt Hoos ,  Josselin Guillozet ,  Gemma Bird ,  Andrew Newell

IPC: G06F21/44

CPC classification number: G06F21/44

Abstract: Methods of verifying a genuine presence and identity of a user of a mobile device include analyzing a stream of data from sensors of the mobile device over a period spanning a user press of a button displayed on a touch screen of the device. Results of the analysis are used to distinguish a genuine user button press from a simulated event generated by device emulation software or other simulation tool. The presence and identity verification are determined in part by the results of the sensor stream analysis, optionally in conjunction with an analysis of biometric data captured by the mobile device when the press of a button on the device touch screen has been elicited. The biometric data may include capture of imagery of a user face or body part by a camera of the mobile device.

公开(公告)号：US20240290079A1

公开(公告)日：2024-08-29

申请号：US18486241

申请日：2023-10-13

Applicant: iProov Limited

Inventor： William J.H. Morgan ,  Panagiotis Papadopoulos ,  Gemma Bird ,  Andrew Newell

IPC: G06V10/776 ,  G06V10/764 ,  G06V10/94 ,  H04L9/40

CPC classification number: G06V10/776 ,  G06V10/764 ,  G06V10/95 ,  H04L63/1466 ,  G06V2201/10

Abstract: A physical camera provenance scoring system issues challenges to a source of video imagery that purports to include a physical camera. The challenges may include requests for camera metadata, requests to change camera parameters, requests to deploy specific camera capabilities, and requests for video imagery. The challenges are designed to elicit responses that can be used to determine a likelihood score that video imagery was captured by a physical camera as opposed to a virtual camera being deployed in an injection attack. The responses may be input to a machine classifier trained on imagery of known provenance to generate the likelihood score. The score may depend on the consistency of responsive camera metadata with received imagery, the behavior and end state of the video source in response to sequences of challenges, the ability of the video source to execute challenges, and the timing and computational load associated with challenge execution.