Recap: Detecting Deepfake Video with Unpredictable Tampered Traces via
  Recovering Faces and Mapping Recovered Faces
Juan Hu Xin Liao Difei Gao Satoshi Tsutsui Qian Wang Zheng Qin Mike Zheng Shou
The exploitation of Deepfake techniques for malicious intentions has driven
significant research interest in Deepfake detection. Deepfake manipulations
frequently introduce random tampered traces, leading to unpredictable outcomes
in different facial regions. However, existing detection methods heavily rely
on specific forgery indicators, and as the forgery mode improves, these traces
become increasingly randomized, resulting in a decline in the detection
performance of methods reliant on specific forgery traces. To address the
limitation, we propose Recap, a novel Deepfake detection model that exposes
unspecific facial part inconsistencies by recovering faces and enlarges the
differences between real and fake by mapping recovered faces. In the recovering
stage, the model focuses on randomly masking regions of interest (ROIs) and
reconstructing real faces without unpredictable tampered traces, resulting in a
relatively good recovery effect for real faces while a poor recovery effect for
fake faces. In the mapping stage, the output of the recovery phase serves as
supervision to guide the facial mapping process. This mapping process
strategically emphasizes the mapping of fake faces with poor recovery, leading
to a further deterioration in their representation, while enhancing and
refining the mapping of real faces with good representation. As a result, this
approach significantly amplifies the discrepancies between real and fake
videos. Our extensive experiments on standard benchmarks demonstrate that Recap
is effective in multiple scenarios.