  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.
