FDS_2D: rethinking magnitude-phase features for DeepFake detection

To reduce the harm of forged information, more and more detection methods use frequency domain information. They mostly take spectra as clues to identify fake content. However, the current work tends 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), which is 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 modules 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 + + , Celeb-DF, and DFDC. It proves that the ability of FDS_2D to detect DeepFake is not inferior to existing models.

Automated summary

To reduce the harm of forged information, more and more detection methods are using frequency domain information. However, the current work tends to use only one spectrum for learning. Therefore, we propose FDS_2D, a multi-branch network that extracts features from different frequency spectra. Experimental results show that FDS_2D effectively detects DeepFake by extracting and utilizing spectral information.