  Facial Liveness Verification (FLV) is widely used for identity authentication
in many security-sensitive domains and offered as Platform-as-a-Service (PaaS)
by leading cloud vendors. Yet, with the rapid advances in synthetic media
techniques (e.g., deepfake), the security of FLV is facing unprecedented
challenges, about which little is known thus far.
  To bridge this gap, in this paper, we conduct the first systematic study on
the security of FLV in real-world settings. Specifically, we present
LiveBugger, a new deepfake-powered attack framework that enables customizable,
automated security evaluation of FLV. Leveraging LiveBugger, we perform a
comprehensive empirical assessment of representative FLV platforms, leading to
a set of interesting findings. For instance, most FLV APIs do not use
anti-deepfake detection; even for those with such defenses, their effectiveness
is concerning (e.g., it may detect high-quality synthesized videos but fail to
detect low-quality ones). We then conduct an in-depth analysis of the factors
impacting the attack performance of LiveBugger: a) the bias (e.g., gender or
race) in FLV can be exploited to select victims; b) adversarial training makes
deepfake more effective to bypass FLV; c) the input quality has a varying
influence on different deepfake techniques to bypass FLV. Based on these
findings, we propose a customized, two-stage approach that can boost the attack
success rate by up to 70%. Further, we run proof-of-concept attacks on several
representative applications of FLV (i.e., the clients of FLV APIs) to
illustrate the practical implications: due to the vulnerability of the APIs,
many downstream applications are vulnerable to deepfake. Finally, we discuss
potential countermeasures to improve the security of FLV. Our findings have
been confirmed by the corresponding vendors.
