  Face recognition (FR) systems have demonstrated outstanding verification
performance, suggesting suitability for real-world applications ranging from
photo tagging in social media to automated border control (ABC). In an advanced
FR system with deep learning-based architecture, however, promoting the
recognition efficiency alone is not sufficient, and the system should also
withstand potential kinds of attacks designed to target its proficiency. Recent
studies show that (deep) FR systems exhibit an intriguing vulnerability to
imperceptible or perceptible but natural-looking adversarial input images that
drive the model to incorrect output predictions. In this article, we present a
comprehensive survey on adversarial attacks against FR systems and elaborate on
the competence of new countermeasures against them. Further, we propose a
taxonomy of existing attack and defense methods based on different criteria. We
compare attack methods on the orientation and attributes and defense approaches
on the category. Finally, we explore the challenges and potential research
direction.
