Real Masks and Spoof Faces: On the Masked Face Presentation Attack Detection
This addresses a security gap in face recognition systems for real-world scenarios where masks are common, though it is incremental as it extends existing PAD research to masked conditions.
The study tackled the problem of face presentation attack detection (PAD) in the context of masked faces during COVID-19, showing that real masked attacks significantly threaten FR system security, with experiments revealing vulnerabilities in seven state-of-the-art PAD algorithms.
Face masks have become one of the main methods for reducing the transmission of COVID-19. This makes face recognition (FR) a challenging task because masks hide several discriminative features of faces. Moreover, face presentation attack detection (PAD) is crucial to ensure the security of FR systems. In contrast to the growing number of masked FR studies, the impact of face masked attacks on PAD has not been explored. Therefore, we present novel attacks with real face 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 different experimental settings. We also evaluate the vulnerability of FR systems to masked attacks. The experiments show that real masked attacks pose a serious threat to the operation and security of FR systems.