Abstract
Face recognition systems are designed to be robust against changes in head pose, illumination, and blurring during image capture. If a malicious person presents a face photo of the registered user, they may bypass the authentication process illegally. Such spoofing attacks need to be detected before face recognition. In this paper, we propose a spoofing attack detection method based on Vision Transformer (ViT) to detect minute differences between live and spoofed face images. The proposed method utilizes the intermediate features of ViT, which have a good balance between local and global features that are important for spoofing attack detection, for calculating loss in training and score in inference. The proposed method also introduces two data augmentation methods: face anti-spoofing data augmentation and patch-wise data augmentation, to improve the accuracy of spoofing attack detection. We demonstrate the effectiveness of the proposed method through experiments using the OULU-NPU and SiW datasets.
Abstract (translated)
面部识别系统旨在适应头部姿态、光照条件以及图像采集时的模糊变化。如果恶意人员使用已注册用户的面部照片，他们可能会非法绕过身份验证过程。此类冒充攻击需要在进行面部识别之前被检测出来。本文提出了一种基于视觉变换器（Vision Transformer, ViT）的冒充攻击检测方法，旨在通过细微差异区分真实人脸图像和伪造的人脸图像。 所提出的方法利用了ViT中间层特征，这些特征在局部和全局特征之间取得了良好的平衡，并且对识别冒充攻击非常重要。此方法将这些特征用于训练中的损失计算以及推理过程中的分数计算。此外，该方法还引入了两种数据增强技术：面部防伪造数据增强及块级数据增强，以提高冒充攻击检测的准确性。 通过使用OULU-NPU和SiW数据集进行实验，我们展示了所提出方法的有效性。
URL
https://arxiv.org/abs/2505.24402
