Generalizable Deepfake Detection With Phase-Based Motion Analysis

We propose PhaseForensics, a DeepFake (DF) video detection method that uses a phase-based motion representation of facial temporal dynamics. Existing methods that rely on temporal information across video frames for DF detection have many advantages over the methods that only utilize the per-frame features. However, these temporal DF detection methods still show limited cross-dataset generalization and robustness to common distortions due to factors such as error-prone motion estimation, inaccurate landmark tracking, or the susceptibility of the pixel intensity-based features to adversarial distortions and the cross-dataset domain shifts. Our key insight to overcome these issues is to leverage the temporal phase variations in the band-pass frequency components of a face region across video frames. This not only enables a robust estimate of the temporal dynamics in the facial regions, but is also less prone to cross-dataset variations. Furthermore, we show that the band-pass filters used to compute the local per-frame phase form an effective defense against the perturbations commonly seen in gradient-based adversarial attacks. Overall, with PhaseForensics, we show improved distortion and adversarial robustness, and state-of-the-art cross-dataset generalization, with 92.4% video-level AUC on the challenging CelebDFv2 benchmark (a recent state of-the-art method, FTCN, compares at 86.9%).