  Synthetically-generated audios and videos -- so-called deep fakes -- continue
to capture the imagination of the computer-graphics and computer-vision
communities. At the same time, the democratization of access to technology that
can create sophisticated manipulated video of anybody saying anything continues
to be of concern because of its power to disrupt democratic elections, commit
small to large-scale fraud, fuel dis-information campaigns, and create
non-consensual pornography. We describe a biometric-based forensic technique
for detecting face-swap deep fakes. This technique combines a static biometric
based on facial recognition with a temporal, behavioral biometric based on
facial expressions and head movements, where the behavioral embedding is
learned using a CNN with a metric-learning objective function. We show the
efficacy of this approach across several large-scale video datasets, as well as
in-the-wild deep fakes.
