  DeepFake detection has so far been dominated by ``artifact-driven'' methods
and the detection performance significantly degrades when either the type of
image artifacts is unknown or the artifacts are simply too hard to find. In
this work, we present an alternative approach: Identity-Driven DeepFake
Detection. Our approach takes as input the suspect image/video as well as the
target identity information (a reference image or video). We output a decision
on whether the identity in the suspect image/video is the same as the target
identity. Our motivation is to prevent the most common and harmful DeepFakes
that spread false information of a targeted person. The identity-based approach
is fundamentally different in that it does not attempt to detect image
artifacts. Instead, it focuses on whether the identity in the suspect
image/video is true. To facilitate research on identity-based detection, we
present a new large scale dataset ``Vox-DeepFake", in which each suspect
content is associated with multiple reference images collected from videos of a
target identity. We also present a simple identity-based detection algorithm
called the OuterFace, which may serve as a baseline for further research. Even
trained without fake videos, the OuterFace algorithm achieves superior
detection accuracy and generalizes well to different DeepFake methods, and is
robust with respect to video degradation techniques -- a performance not
achievable with existing detection algorithms.
