AdvFAS: A robust face anti-spoofing framework against adversarial examples
arxiv(2023)
摘要
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.
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