Improving Deepfake Detectors against Real-world Perturbations with Amplitude-Phase Switch Augmentation

In recent years, the remarkable progress in facial manipulation techniques has raised social concerns due to their potential malicious usage and has received considerable attention from both industry and academia. While current deep learning-based face forgery detection methods have achieved promising results, their performance often degrades drastically when they are tested in non-trivial situations under realistic perturbations. This paper proposes to leverage the information in the frequency domain, particularly the phase spectrum, to better differentiate between deepfakes and authentic images. Specifically, a new augmentation method called degradation-based amplitude-phase switch (DAPS) is proposed, which disregards the sensitive amplitude spectrum of a forged facial image and enforces the detection network to focus on phase components during the training process. Extensive evaluation results from a realistic assessment framework show that the proposed augmentation method significantly improves the robustness of two deepfake detectors analyzed and consistently outperform other augmentation approaches under various perturbations.