Identity-Driven DeepFake Detection

Understand

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.