Delocate: Detection and Localization for Deepfake Videos with
  Randomly-Located Tampered Traces
Juan Hu Xin Liao Difei Gao Satoshi Tsutsui Qian Wang Zheng Qin Mike Zheng Shou
Deepfake videos are becoming increasingly realistic, showing subtle tampering
traces on facial areasthat vary between frames. Consequently, many existing
Deepfake detection methods struggle to detect unknown domain Deepfake videos
while accurately locating the tampered region. To address thislimitation, we
propose Delocate, a novel Deepfake detection model that can both recognize
andlocalize unknown domain Deepfake videos. Ourmethod consists of two stages
named recoveringand localization. In the recovering stage, the modelrandomly
masks regions of interest (ROIs) and reconstructs real faces without tampering
traces, resulting in a relatively good recovery effect for realfaces and a poor
recovery effect for fake faces. Inthe localization stage, the output of the
recoveryphase and the forgery ground truth mask serve assupervision to guide
the forgery localization process. This process strategically emphasizes the
recovery phase of fake faces with poor recovery, facilitating the localization
of tampered regions. Ourextensive experiments on four widely used benchmark
datasets demonstrate that Delocate not onlyexcels in localizing tampered areas
but also enhances cross-domain detection performance.