  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.
