  Maliciously-manipulated images or videos - so-called deep fakes - especially
face-swap images and videos have attracted more and more malicious attackers to
discredit some key figures. Previous pixel-level artifacts based detection
techniques always focus on some unclear patterns but ignore some available
semantic clues. Therefore, these approaches show weak interpretability and
robustness. In this paper, we propose a biometric information based method to
fully exploit the appearance and shape feature for face-swap detection of key
figures. The key aspect of our method is obtaining the inconsistency of 3D
facial shape and facial appearance, and the inconsistency based clue offers
natural interpretability for the proposed face-swap detection method.
Experimental results show the superiority of our method in robustness on
various laundering and cross-domain data, which validates the effectiveness of
the proposed method.
