  As the GAN-based face image and video generation techniques, widely known as
DeepFakes, have become more and more matured and realistic, there comes a
pressing and urgent demand for effective DeepFakes detectors. Motivated by the
fact that remote visual photoplethysmography (PPG) is made possible by
monitoring the minuscule periodic changes of skin color due to blood pumping
through the face, we conjecture that normal heartbeat rhythms found in the real
face videos will be disrupted or even entirely broken in a DeepFake video,
making it a potentially powerful indicator for DeepFake detection. In this
work, we propose DeepRhythm, a DeepFake detection technique that exposes
DeepFakes by monitoring the heartbeat rhythms. DeepRhythm utilizes
dual-spatial-temporal attention to adapt to dynamically changing face and fake
types. Extensive experiments on FaceForensics++ and DFDC-preview datasets have
confirmed our conjecture and demonstrated not only the effectiveness, but also
the generalization capability of \emph{DeepRhythm} over different datasets by
various DeepFakes generation techniques and multifarious challenging
degradations.
