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.

翻译：目前COVID-19大流行的COVID-19大流行已导致大规模的公共卫生问题,面罩已成为减少冠状病毒传播的最有效方法之一,这使面部识别成为一项挑战性的任务,因为有几种歧视特征被隐藏。此外,面部显示攻击探测(PAD)对于确保FR系统的安全至关重要。与越来越多的蒙面的FR研究相比,蒙面攻击对PAD的影响没有被探讨。因此,我们用面罩对情况介绍和攻击的主体进行新的攻击,用真实面罩进行真实攻击,以反映当前现实世界的情况。此外,本研究通过使用七种最先进的PAD算法,在数据库内和跨数据库的情景下调查PAD演算法对PAD表演的影响。我们还评估FR系统对蒙面攻击的脆弱性。实验表明,真实的蒙面攻击对FR系统的运作和安全构成严重威胁。