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DeepFakes are synthetic videos generated by swapping a face of an originalimage with the face of somebody else. In this paper, we describe our work todevelop general, deep learning-based models to classify DeepFake content. Wepropose a novel framework for using Generative Adversarial Network (GAN)-basedmodels, we call MRI-GAN, that utilizes perceptual differences in images todetect synthesized videos. We test our MRI-GAN approach and aplain-frames-based model using the DeepFake Detection Challenge Dataset. Ourplain frames-based-model achieves 91% test accuracy and a model which uses ourMRI-GAN framework with Structural Similarity Index Measurement (SSIM) for theperceptual differences achieves 74% test accuracy. The results of MRI-GAN arepreliminary and may be improved further by modifying the choice of lossfunction, tuning hyper-parameters, or by using a more advanced perceptualsimilarity metric.

MRI-GAN: A Generalized Approach to Detect DeepFakes using Perceptual Image Assessment