  Fake portrait video generation techniques have been posing a new threat to
the society with photorealistic deep fakes for political propaganda, celebrity
imitation, forged evidences, and other identity related manipulations.
Following these generation techniques, some detection approaches have also been
proved useful due to their high classification accuracy. Nevertheless, almost
no effort was spent to track down the source of deep fakes. We propose an
approach not only to separate deep fakes from real videos, but also to discover
the specific generative model behind a deep fake. Some pure deep learning based
approaches try to classify deep fakes using CNNs where they actually learn the
residuals of the generator. We believe that these residuals contain more
information and we can reveal these manipulation artifacts by disentangling
them with biological signals. Our key observation yields that the
spatiotemporal patterns in biological signals can be conceived as a
representative projection of residuals. To justify this observation, we extract
PPG cells from real and fake videos and feed these to a state-of-the-art
classification network for detecting the generative model per video. Our
results indicate that our approach can detect fake videos with 97.29% accuracy,
and the source model with 93.39% accuracy.
