Abstract: Systems and methods for detecting an injection attack during a digital identity verification session are provided. The techniques include obtaining video frames and/or still image frames acquired using a camera of a mobile device, the video frames and/or the still image frames including images of a user and/or an identification document, and obtaining inertial data acquired using an inertial measurement unit (IMU) of the mobile device during the digital identity verification session. The techniques also include determining, using the video frames and/or the still image frames and the inertial data, whether the digital identity verification session is subject to the injection attack.

Abstract: A system for remote identification of users. The system uses deep learning techniques for authenticating a user from an identification document and using automated verification of identification documents. Identification documents may be authenticated by validating security features. The system may determine features expected in a valid identification document and determine whether those features are present, employing techniques, such as determining whether direction-sensitive features are present. Liveness of a user indicated by the identification document may be determined with a deep learning model trained for identification of facial spoofing attacks.

Abstract: A system for remote identification of users. The system uses deep learning techniques for authenticating a user from an identification document, using automated verification of identification documents and detection that a live person identified by the document is present. Liveness of a user indicated by the identification document may be determined with a deep learning model trained for identification of facial spoofing attacks. The deep learning model may be trained using training data extracted from facial feature locations of training images.