  Recently, significant advancements have been made in face recognition
technologies using Deep Neural Networks. As a result, companies such as
Microsoft, Amazon, and Naver offer highly accurate commercial face recognition
web services for diverse applications to meet the end-user needs. Naturally,
however, such technologies are threatened persistently, as virtually any
individual can quickly implement impersonation attacks. In particular, these
attacks can be a significant threat for authentication and identification
services, which heavily rely on their underlying face recognition technologies'
accuracy and robustness. Despite its gravity, the issue regarding deepfake
abuse using commercial web APIs and their robustness has not yet been
thoroughly investigated. This work provides a measurement study on the
robustness of black-box commercial face recognition APIs against Deepfake
Impersonation (DI) attacks using celebrity recognition APIs as an example case
study. We use five deepfake datasets, two of which are created by us and
planned to be released. More specifically, we measure attack performance based
on two scenarios (targeted and non-targeted) and further analyze the differing
system behaviors using fidelity, confidence, and similarity metrics.
Accordingly, we demonstrate how vulnerable face recognition technologies from
popular companies are to DI attack, achieving maximum success rates of 78.0%
and 99.9% for targeted (i.e., precise match) and non-targeted (i.e., match with
any celebrity) attacks, respectively. Moreover, we propose practical defense
strategies to mitigate DI attacks, reducing the attack success rates to as low
as 0% and 0.02% for targeted and non-targeted attacks, respectively.
