FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals

The recent proliferation of fake portrait videos poses direct threats on society, law, and privacy [1]. 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…

• NS
  National Science Foundation

  Award: CNS-1629898