Deepfake Face Provenance for Proactive Forensics

Malicious deepfake face not only violates the privacy of personal identities, but also confuses the public and causes huge social harm. The current deepfake detection only stays at the level of distinguishing between true and false, but cannot trace the original genuine face corresponding to the fake face, that is, it does not have the ability to trace the source of evidence. The deepfake countermeasure technology for judicial forensics urgently calls for deepfake inversion. This paper pioneers an interesting question about face deepfake, active forensics that "know what it is and how it happened". Given that deepfake faces do not completely discard the features of original faces, especially facial expressions and poses, we argue that original faces can be approximately speculated from their deepfake counterparts. Correspondingly, we design a disentangling reversing network that decouples latent space features of deepfake faces under the supervision of real-fake face pair samples to infer original faces in reverse.