用DINOv2+寄存器检测人脸活体，精准识别照片欺骗
Optimizing DINOv2 with Registers for Face Anti-Spoofing
- 在DINOv2中引入寄存器，增强特征泛化能力并抑制注意力干扰
- 在ICCV2025和SiW数据集上达到领先性能，有效区分真实与伪造人脸
- 适合需要高安全性的生物识别系统开发者使用
人脸识别系统虽能应对姿态、光照和模糊等变化，但易被用户照片等伪造攻击绕过。本文提出基于DINOv2的活体检测方法，通过引入寄存器提取可迁移特征，并抑制注意力机制中的扰动，使模型聚焦于细微差异。实验在ICCV2025“统一物理-数字攻击检测”挑战赛数据集和SiW数据集上验证，显著提升对照片欺骗等攻击的检测能力。项目主页：https://gsisaoki.github.io/FAS-DINOv2-ICCVW/
原文摘要 · Abstract (English)
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. The project page is available at: https://gsisaoki.github.io/FAS-DINOv2-ICCVW/ .
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