AdvFAS: A robust face anti-spoofing framework against adversarial examples

TL;DR

AdvFAS introduces a robust face anti-spoofing framework that effectively detects presentation attacks and adversarial examples by leveraging coupled scores, demonstrating high accuracy across various settings and real-world scenarios.

Contribution

The paper proposes AdvFAS, a novel face anti-spoofing framework that couples detection scores to improve robustness against adversarial attacks and real-world spoofing.

Findings

Effective detection across multiple datasets and attack types

High accuracy on clean face images

Successful real-world adversarial example detection

Abstract

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.