用ViT中间特征检测人脸伪造，提升识别安全性。
Leveraging Intermediate Features of Vision Transformer for Face Anti-Spoofing
- 利用ViT中间层特征融合局部与全局信息
- 在OULU-NPU和SiW数据集上准确率超现有方法
- 适合需要高安全性的生物识别系统应用
人脸识别系统需抵御姿态、光照、模糊等变化。若攻击者使用注册用户的照片进行欺骗，可能非法通过认证。本文提出一种基于视觉变换器（ViT）的伪造攻击检测方法，利用其中间特征在局部与全局信息间取得良好平衡，用于训练时的损失计算和推理时的评分。该方法还引入两种数据增强策略：针对伪造检测的数据增强和逐块数据增强，以提升检测精度。通过在OULU-NPU和SiW数据集上的实验验证了方法的有效性。
原文摘要 · Abstract (English)
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. The project page is available at: https://gsisaoki.github.io/FAS-ViT-CVPRW/ .
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