  DeepFakes are synthetic videos generated by swapping a face of an original
image with the face of somebody else. In this paper, we describe our work to
develop general, deep learning-based models to classify DeepFake content. We
propose a novel framework for using Generative Adversarial Network (GAN)-based
models, we call MRI-GAN, that utilizes perceptual differences in images to
detect synthesized videos. We test our MRI-GAN approach and a
plain-frames-based model using the DeepFake Detection Challenge Dataset. Our
plain frames-based-model achieves 91% test accuracy and a model which uses our
MRI-GAN framework with Structural Similarity Index Measurement (SSIM) for the
perceptual differences achieves 74% test accuracy. The results of MRI-GAN are
preliminary and may be improved further by modifying the choice of loss
function, tuning hyper-parameters, or by using a more advanced perceptual
similarity metric.
