  In this work, we describe a new deep learning based method that can
effectively distinguish AI-generated fake videos (referred to as {\em DeepFake}
videos hereafter) from real videos. Our method is based on the observations
that current DeepFake algorithm can only generate images of limited
resolutions, which need to be further warped to match the original faces in the
source video. Such transforms leave distinctive artifacts in the resulting
DeepFake videos, and we show that they can be effectively captured by
convolutional neural networks (CNNs). Compared to previous methods which use a
large amount of real and DeepFake generated images to train CNN classifier, our
method does not need DeepFake generated images as negative training examples
since we target the artifacts in affine face warping as the distinctive feature
to distinguish real and fake images. The advantages of our method are two-fold:
(1) Such artifacts can be simulated directly using simple image processing
operations on a image to make it as negative example. Since training a DeepFake
model to generate negative examples is time-consuming and resource-demanding,
our method saves a plenty of time and resources in training data collection;
(2) Since such artifacts are general existed in DeepFake videos from different
sources, our method is more robust compared to others. Our method is evaluated
on two sets of DeepFake video datasets for its effectiveness in practice.
