Improving Cross-dataset Deepfake Detection with Deep Information
  Decomposition
Shanmin Yang Shu Hu Bin Zhu Ying Fu Siwei Lyu Xi Wu Xin Wang
Deepfake technology poses a significant threat to security and social trust.
Although existing detection methods have demonstrated high performance in
identifying forgeries within datasets using the same techniques for training
and testing, they suffer from sharp performance degradation when faced with
cross-dataset scenarios where unseen deepfake techniques are tested. To address
this challenge, we propose a deep information decomposition (DID) framework in
this paper. Unlike most existing deepfake detection methods, our framework
prioritizes high-level semantic features over visual artifacts. Specifically,
it decomposes facial features into deepfake-related and irrelevant information
and optimizes the deepfake information for real/fake discrimination to be
independent of other factors. Our approach improves the robustness of deepfake
detection against various irrelevant information changes and enhances the
generalization ability of the framework to detect unseen forgery methods.
Extensive experimental comparisons with existing state-of-the-art detection
methods validate the effectiveness and superiority of the DID framework on
cross-dataset deepfake detection.