  We propose PhaseForensics, a DeepFake (DF) video detection method that
leverages a phase-based motion representation of facial temporal dynamics.
Existing methods relying on temporal inconsistencies for DF detection present
many advantages over the typical frame-based methods. However, they still show
limited cross-dataset generalization and robustness to common distortions.
These shortcomings are partially due to error-prone motion estimation and
landmark tracking, or the susceptibility of the pixel intensity-based features
to spatial 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 components of the Complex Steerable Pyramid on face sub-regions. This
not only enables a robust estimate of the temporal dynamics in these regions,
but is also less prone to cross-dataset variations. Furthermore, 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 91.2%
video-level AUC on the challenging CelebDFv2 (a recent state-of-the-art
compares at 86.9%).
