  The recent proliferation of fake portrait videos poses direct threats on
society, law, and privacy. Believing the fake video of a politician,
distributing fake pornographic content of celebrities, fabricating impersonated
fake videos as evidence in courts are just a few real world consequences of
deep fakes. We present a novel approach to detect synthetic content in portrait
videos, as a preventive solution for the emerging threat of deep fakes. In
other words, we introduce a deep fake detector. We observe that detectors
blindly utilizing deep learning are not effective in catching fake content, as
generative models produce formidably realistic results. Our key assertion
follows that biological signals hidden in portrait videos can be used as an
implicit descriptor of authenticity, because they are neither spatially nor
temporally preserved in fake content. To prove and exploit this assertion, we
first engage several signal transformations for the pairwise separation
problem, achieving 99.39% accuracy. Second, we utilize those findings to
formulate a generalized classifier for fake content, by analyzing proposed
signal transformations and corresponding feature sets. Third, we generate novel
signal maps and employ a CNN to improve our traditional classifier for
detecting synthetic content. Lastly, we release an "in the wild" dataset of
fake portrait videos that we collected as a part of our evaluation process. We
evaluate FakeCatcher on several datasets, resulting with 96%, 94.65%, 91.50%,
and 91.07% accuracies, on Face Forensics, Face Forensics++, CelebDF, and on our
new Deep Fakes Dataset respectively. We also analyze signals from various
facial regions, under image distortions, with varying segment durations, from
different generators, against unseen datasets, and under several dimensionality
reduction techniques.
