AdvFAS: A robust face anti-spoofing framework against adversarial examples
This addresses the reliability of face recognition systems against adversarial attacks, representing an incremental improvement in a domain-specific area.
The paper tackles the problem of defending face anti-spoofing systems against adversarial examples by proposing AdvFAS, a framework that uses coupled scores to distinguish correctly and wrongly detected face images, achieving high accuracy in various settings and real-world applications.
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.