CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition

Understand

Deepfake technology poses a significant threat to security and social trust.

• Although existing detection methods have shown high performance in identifying forgeries within datasets that use the same deepfake techniques for both 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 to enhance the performance of Cross-dataset Deepfake Detection (CrossDF).
• Unlike most existing deepfake detection methods, our framework prioritizes high-level semantic features over specific visual artifacts.