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.

我们提出了一种深度信息分解（DID）框架，旨在通过优化深度信息以实现对真实/伪造的区分来解决深假技术跨数据集检测中面临的挑战，并改善该框架对各种无关信息变化的鲁棒性和检测未见伪造技术的泛化能力。与现有方法进行广泛的实验对比验证了DID框架在跨数据集深假检测方面的有效性和优越性。

优化跨数据集深度伪造检测的深度信息分解