Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification
Erkka Rantahalvari, Olli Silv\'en, Zinelabidine Boulkenafet, Constantino \'Alvarez Casado

TL;DR
This study explores using passive motion traces from selfies as an auxiliary signal to improve detection of deepfakes and injection attacks in mobile identity verification systems.
Contribution
It introduces CanSelfie, a new dataset and benchmarking of classifiers for spoof detection and user verification using multi-sensor motion data.
Findings
Accelerometer data alone can effectively reject stationary attack proxies.
Certain classifiers achieve low false acceptance rates in spoof screening.
Motion traces contain measurable information for both spoof detection and user verification.
Abstract
Mobile remote identity verification (RIdV) systems are exposed to attacks that manipulate or replace the facial video stream, including presentation attacks, real-time deepfakes, and video injection. Recent European requirements, including ETSI TS 119 461 and CEN/TS 18099, motivate complementary evidence channels beyond camera-based presentation-attack detection. This paper investigates whether passive motion traces recorded during selfie capture provide auxiliary evidence for spoof screening and user verification. We introduce CanSelfie, a dataset of 375 bona fide multi-sensor sequences collected at 50\,Hz from 30 participants using a commercial mobile RIdV application, together with stationary, handheld, and temporally shifted attack-proxy scenarios. We benchmark 7 multivariate time-series classifiers and 8 whole-series anomaly detectors across sensor configurations and temporal…
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