  With the emergence of GAN, face forgery technologies have been heavily
abused. Achieving accurate face forgery detection is imminent. Inspired by
remote photoplethysmography (rPPG) that PPG signal corresponds to the periodic
change of skin color caused by heartbeat in face videos, we observe that
despite the inevitable loss of PPG signal during the forgery process, there is
still a mixture of PPG signals in the forgery video with a unique rhythmic
pattern depending on its generation method. Motivated by this key observation,
we propose a framework for face forgery detection and categorization consisting
of: 1) a Spatial-Temporal Filtering Network (STFNet) for PPG signals filtering,
and 2) a Spatial-Temporal Interaction Network (STINet) for constraint and
interaction of PPG signals. Moreover, with insight into the generation of
forgery methods, we further propose intra-source and inter-source blending to
boost the performance of the framework. Overall, extensive experiments have
proved the superiority of our method.
