  Recent DeepFake detection methods have shown excellent performance on public
datasets but are significantly degraded on new forgeries. Solving this problem
is important, as new forgeries emerge daily with the continuously evolving
generative techniques. Many efforts have been made for this issue by seeking
the commonly existing traces empirically on data level. In this paper, we
rethink this problem and propose a new solution from the unsupervised domain
adaptation perspective. Our solution, called DomainForensics, aims to transfer
the forgery knowledge from known forgeries to new forgeries. Unlike recent
efforts, our solution does not focus on data view but on learning strategies of
DeepFake detectors to capture the knowledge of new forgeries through the
alignment of domain discrepancies. In particular, unlike the general domain
adaptation methods which consider the knowledge transfer in the semantic class
category, thus having limited application, our approach captures the subtle
forgery traces. We describe a new bi-directional adaptation strategy dedicated
to capturing the forgery knowledge across domains. Specifically, our strategy
considers both forward and backward adaptation, to transfer the forgery
knowledge from the source domain to the target domain in forward adaptation and
then reverse the adaptation from the target domain to the source domain in
backward adaptation. In forward adaptation, we perform supervised training for
the DeepFake detector in the source domain and jointly employ adversarial
feature adaptation to transfer the ability to detect manipulated faces from
known forgeries to new forgeries. In backward adaptation, we further improve
the knowledge transfer by coupling adversarial adaptation with
self-distillation on new forgeries. This enables the detector to expose new
forgery features from unlabeled data and avoid forgetting the known knowledge
of known...
