  The emergence of Internet of Things (IoT) brings about new security
challenges at the intersection of cyber and physical spaces. One prime example
is the vulnerability of Face Recognition (FR) based access control in IoT
systems. While previous research has shown that Deep Neural Network(DNN)-based
FR systems (FRS) are potentially susceptible to imperceptible impersonation
attacks, the potency of such attacks in a wide set of scenarios has not been
thoroughly investigated. In this paper, we present the first systematic,
wide-ranging measurement study of the exploitability of DNN-based FR systems
using a large scale dataset. We find that arbitrary impersonation attacks,
wherein an arbitrary attacker impersonates an arbitrary target, are hard if
imperceptibility is an auxiliary goal. Specifically, we show that factors such
as skin color, gender, and age, impact the ability to carry out an attack on a
specific target victim, to different extents. We also study the feasibility of
constructing universal attacks that are robust to different poses or views of
the attacker's face. Our results show that finding a universal perturbation is
a much harder problem from the attacker's perspective. Finally, we find that
the perturbed images do not generalize well across different DNN models. This
suggests security countermeasures that can dramatically reduce the
exploitability of DNN-based FR systems.
