Optimizing DINOv2 with Registers for Face Anti-Spoofing

• URL: http://arxiv.org/abs/2510.17201v1
• Date: Mon, 20 Oct 2025 06:27:02 GMT
• Title: Optimizing DINOv2 with Registers for Face Anti-Spoofing
• Authors: Mika Feng, Pierre Gallin-Martel, Koichi Ito, Takafumi Aoki,
• Abstract summary: Malicious actors can exploit face recognition systems by presenting a face photo of a registered user.<n>We propose a DINOv2-based spoofing attack detection method to discern minute differences between live and spoofed face images.
• Score: 2.099922236065961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face recognition systems are designed to be robust against variations in head pose, illumination, and image blur during capture. However, malicious actors can exploit these systems by presenting a face photo of a registered user, potentially bypassing the authentication process. Such spoofing attacks must be detected prior to face recognition. In this paper, we propose a DINOv2-based spoofing attack detection method to discern minute differences between live and spoofed face images. Specifically, we employ DINOv2 with registers to extract generalizable features and to suppress perturbations in the attention mechanism, which enables focused attention on essential and minute features. We demonstrate the effectiveness of the proposed method through experiments conducted on the dataset provided by ``The 6th Face Anti-Spoofing Workshop: Unified Physical-Digital Attacks Detection@ICCV2025'' and SiW dataset.

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