  The physical attack has been regarded as a kind of threat against real-world
computer vision systems. Still, many existing defense methods are only useful
for small perturbations attacks and can't detect physical attacks effectively.
In this paper, we propose a random-patch based defense strategy to robustly
detect physical attacks for Face Recognition System (FRS). Different from
mainstream defense methods which focus on building complex deep neural networks
(DNN) to achieve high recognition rate on attacks, we introduce a patch based
defense strategy to a standard DNN aiming to obtain robust detection models.
Extensive experimental results on the employed datasets show the superiority of
the proposed defense method on detecting white-box attacks and adaptive attacks
which attack both FRS and the defense method. Additionally, due to the
simpleness yet robustness of our method, it can be easily applied to the real
world face recognition system and extended to other defense methods to boost
the detection performance.
