FakeTracer: Catching Face-swap DeepFakes via Implanting Traces in Training

TL;DR

FakeTracer is a proactive method that implants unique traces into training data to effectively detect face-swap DeepFake videos, addressing the complexity of identity change and unsupervised training.

Contribution

It introduces two types of traces, sustainable and erasable, to be embedded during training, enabling reliable detection of face-swap DeepFakes.

Findings

Effective in exposing face-swap DeepFakes

Robust against various DeepFake generation techniques

Outperforms existing detection methods

Abstract

Face-swap DeepFake is an emerging AI-based face forgery technique that can replace the original face in a video with a generated face of the target identity while retaining consistent facial attributes such as expression and orientation. Due to the high privacy of faces, the misuse of this technique can raise severe social concerns, drawing tremendous attention to defend against DeepFakes recently. In this paper, we describe a new proactive defense method called FakeTracer to expose face-swap DeepFakes via implanting traces in training. Compared to general face-synthesis DeepFake, the face-swap DeepFake is more complex as it involves identity change, is subjected to the encoding-decoding process, and is trained unsupervised, increasing the difficulty of implanting traces into the training phase. To effectively defend against face-swap DeepFake, we design two types of traces, sustainable trace (STrace) and erasable trace (ETrace), to be added to training faces. During the training, these manipulated faces affect the learning of the face-swap DeepFake model, enabling it to generate faces that only contain sustainable traces. In light of these two traces, our method can effectively expose DeepFakes by identifying them. Extensive experiments corroborate the efficacy of our method on defending against face-swap DeepFake.