Real Masks and Fake Faces: On the Masked Face Presentation Attack Detection
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The ongoing COVID-19 pandemic has lead to massive public health issues. Face masks have become one of the most efficient ways to reduce coronavirus transmission. This makes face recognition (FR) a challenging task as several discriminative features are hidden. Moreover, face presentation attack detection (PAD) is crucial to ensure the security of FR systems. In contrast to growing numbers of masked FR studies, the impact of masked attacks on PAD has not been explored. Therefore, we present novel attacks with real masks placed on presentations and attacks with subjects wearing masks to reflect the current real-world situation. Furthermore, this study investigates the effect of masked attacks on PAD performance by using seven state-of-the-art PAD algorithms under intra- and cross-database scenarios. We also evaluate the vulnerability of FR systems on masked attacks. The experiments show that real masked attacks pose a serious threat to the operation and security of FR systems.
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