FDS_2D: Rethinking magnitude-phase features for DeepFake Detection

Abstract The detection method based on the frequency domain is one of the essential methods to reduce the harm of forged information. This method mostly uses spectra as clues to identify fake content. However, the current methods tend to use only one of the magnitude and phase spectra for learning. In this paper, we notice that the magnitude and phase spectrum contain different image information. Only one spectrum is easily disturbed by noise, and the robustness of the method is difficult to guarantee. Therefore, we propose the Frequency Domain Separable DeepFake Detection (FDS_2D) to use a multi-branch network to obtain features in different frequency spectra. In FDS_2D, the spectral information is divided into three categories: the magnitude spectrum, the phase spectrum, and the relationship between the two spectra. According to their characteristics, we design independent methods for feature extraction from them. Moreover, to improve the utilization efficiency of multi-features, we propose a multi-input multi-output attention mechanism for information interaction between branches. The experimental results show that each part of FDS_2D effectively extracts and applies spectral information; The comprehensive performance of our model is verified on FaceForensic++, CelebDF, and DFDC. It proves that the ability of FDS_2D to detect DeepFake is not inferior to existing models.