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.
Abstract (translated)
面部识别系统被设计成能够抵抗捕捉过程中头部姿态、光照变化和图像模糊带来的影响。然而，恶意行为者可以通过向系统展示已注册用户的面部照片来利用这些系统，从而可能绕过身份验证过程。这样的伪装攻击必须在进行面部识别之前就被检测到。 本文提出了一种基于DINOv2的伪造攻击检测方法，用于区分真实人脸和伪造人脸图像之间的细微差别。具体来说，我们使用带有登记功能的DINOv2来提取可泛化的特征，并抑制注意力机制中的扰动，从而能够在关键和微小细节上集中注意力。 通过在由“第六届面部防伪研讨会：统一物理-数字攻击检测@ICCV2025”提供的数据集以及SiW数据集上的实验，我们展示了所提出方法的有效性。
URL
https://arxiv.org/abs/2510.17201
