  This work introduces a novel DeepFake detection framework based on
physiological measurement. In particular, we consider information related to
the heart rate using remote photoplethysmography (rPPG). rPPG methods analyze
video sequences looking for subtle color changes in the human skin, revealing
the presence of human blood under the tissues. In this work we investigate to
what extent rPPG is useful for the detection of DeepFake videos.
  The proposed fake detector named DeepFakesON-Phys uses a Convolutional
Attention Network (CAN), which extracts spatial and temporal information from
video frames, analyzing and combining both sources to better detect fake
videos. This detection approach has been experimentally evaluated using the
latest public databases in the field: Celeb-DF and DFDC. The results achieved,
above 98% AUC (Area Under the Curve) on both databases, outperform the state of
the art and prove the success of fake detectors based on physiological
measurement to detect the latest DeepFake videos.
