LAUSR

Recent threats to deep learning-based biometric authentication systems stem from adversarial attacks exploiting vulnerabilities in deep learning models. While existing studies extensively analyze the risk of such attacks, they primarily focus on isolated modules (e.g., liveness detectors or identity matchers) or specific adversarial attack types (e.g., evasion and poisoning attacks). In this paper, we introduce a novel approach that comprehensively assesses the risk of adversarial attacks by simulating multiple scenarios within biometric authentication systems. We identify the surfaces susceptible to adversarial attacks within these systems and devise scenarios that reflect the dependencies between modules. Moreover, we establish evaluation metrics to comprehensively assess the risk involved. Through a case study conducted on a real-world face recognition system, we successfully demonstrate the effectiveness of our approach. Our approach facilitates the systematic evaluation of the security of target biometric authentication systems against adversarial attacks. Ultimately, it enables the establishment of robust and proactive defense mechanisms.