Real Masks and Spoof Faces: On the Masked Face Presentation Attack Detection

TL;DR

This paper investigates the impact of real masked face presentation attacks on face recognition security, revealing that masked attacks significantly threaten the effectiveness of current PAD systems and FR security.

Contribution

It introduces novel masked face attack methods and evaluates their impact on state-of-the-art PAD algorithms and face recognition systems.

Findings

Masked attacks significantly reduce PAD performance.

Real masked attacks pose a serious threat to FR security.

Current PAD algorithms are vulnerable to masked face attacks.

Abstract

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.