AdvFAS: A robust face anti-spoofing framework against adversarial
  examples
Jiawei Chen Xiao Yang Heng Yin Mingzhi Ma Bihui Chen Jianteng Peng Yandong Guo Zhaoxia Yin Hang Su
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.