  Recent advances in video manipulation techniques have made the generation of
fake videos more accessible than ever before. Manipulated videos can fuel
disinformation and reduce trust in media. Therefore detection of fake videos
has garnered immense interest in academia and industry. Recently developed
Deepfake detection methods rely on deep neural networks (DNNs) to distinguish
AI-generated fake videos from real videos. In this work, we demonstrate that it
is possible to bypass such detectors by adversarially modifying fake videos
synthesized using existing Deepfake generation methods. We further demonstrate
that our adversarial perturbations are robust to image and video compression
codecs, making them a real-world threat. We present pipelines in both white-box
and black-box attack scenarios that can fool DNN based Deepfake detectors into
classifying fake videos as real.
