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arXiv Machine Learning By Erkka Rantahalvari, Olli Silv\'en, Zinelabidine Boulkenafet, Constantino \'Alvarez Casado

Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification

Read the original on arXiv Machine Learning →

The paper investigates whether passive motion traces recorded during selfie capture can serve as an auxiliary signal for detecting spoofing and verifying users in mobile remote identity verification systems. It introduces the CanSelfie dataset, comprising 375 multi‑sensor sequences from 30 participants, and evaluates seven time‑series classifiers and eight anomaly detectors across various sensor configurations. Results show that accelerometer‑only classifiers achieve very low false rejection rates, while certain models achieve low false acceptance rates and high verification accuracy, indicating that selfie‑capture motion is a promising low‑friction evidence channel.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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