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As the GAN-based face image and video generation techniques, widely known asDeepFakes, have become more and more matured and realistic, there comes apressing and urgent demand for effective DeepFakes detectors. Motivated by thefact that remote visual photoplethysmography (PPG) is made possible bymonitoring the minuscule periodic changes of skin color due to blood pumpingthrough the face, we conjecture that normal heartbeat rhythms found in the realface videos will be disrupted or even entirely broken in a DeepFake video,making it a potentially powerful indicator for DeepFake detection. In thiswork, we propose DeepRhythm, a DeepFake detection technique that exposesDeepFakes by monitoring the heartbeat rhythms. DeepRhythm utilizesdual-spatial-temporal attention to adapt to dynamically changing face and faketypes. Extensive experiments on FaceForensics++ and DFDC-preview datasets haveconfirmed our conjecture and demonstrated not only the effectiveness, but alsothe generalization capability of \emph{DeepRhythm} over different datasets byvarious DeepFakes generation techniques and multifarious challengingdegradations.

DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms