Ensuring the reliability of face recognition systems against presentation attacks necessitates the deployment of face anti-spoofing techniques. Despite considerable advancements in this domain, the ability of even the most state-of-the-art methods to defend against adversarial examples remains elusive. While several adversarial defense strategies have been proposed, they typically suffer from constrained practicability due to inevitable trade-offs between universality, effectiveness, and efficiency. To overcome these challenges, we thoroughly delve into the coupled relationship between adversarial detection and face anti-spoofing. Based on this, we propose a robust face anti-spoofing framework, namely AdvFAS, that leverages two coupled scores to accurately distinguish between correctly detected and wrongly detected face images. Extensive experiments demonstrate the effectiveness of our framework in a variety of settings, including different attacks, datasets, and backbones, meanwhile enjoying high accuracy on clean examples. Moreover, we successfully apply the proposed method to detect real-world adversarial examples.
@article{arxiv.2308.02116,
title = {AdvFAS: A robust face anti-spoofing framework against adversarial examples},
author = {Jiawei Chen and Xiao Yang and Heng Yin and Mingzhi Ma and Bihui Chen and Jianteng Peng and Yandong Guo and Zhaoxia Yin and Hang Su},
journal= {arXiv preprint arXiv:2308.02116},
year = {2024}
}