  The challenge in sourcing attribution for forgery faces has gained widespread
attention due to the rapid development of generative techniques. While many
recent works have taken essential steps on GAN-generated faces, more
threatening attacks related to identity swapping or expression transferring are
still overlooked. And the forgery traces hidden in unknown attacks from the
open-world unlabeled faces still remain under-explored. To push the related
frontier research, we introduce a new benchmark called Open-World DeepFake
Attribution (OW-DFA), which aims to evaluate attribution performance against
various types of fake faces under open-world scenarios. Meanwhile, we propose a
novel framework named Contrastive Pseudo Learning (CPL) for the OW-DFA task
through 1) introducing a Global-Local Voting module to guide the feature
alignment of forged faces with different manipulated regions, 2) designing a
Confidence-based Soft Pseudo-label strategy to mitigate the pseudo-noise caused
by similar methods in unlabeled set. In addition, we extend the CPL framework
with a multi-stage paradigm that leverages pre-train technique and iterative
learning to further enhance traceability performance. Extensive experiments
verify the superiority of our proposed method on the OW-DFA and also
demonstrate the interpretability of deepfake attribution task and its impact on
improving the security of deepfake detection area.
