This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face. Traditional image forensics techniques are usually not well suited to videos due to the compression that strongly degrades the data. Thus, this paper follows a deep learning approach and presents two networks, both with a low number of layers to focus on the mesoscopic properties of images. We evaluate those fast networks on both an existing dataset and a dataset we have constituted from online videos. The tests demonstrate a very successful detection rate with more than 98% for Deepfake and 95% for Face2Face.
@article{arxiv.1809.00888,
title = {MesoNet: a Compact Facial Video Forgery Detection Network},
author = {Darius Afchar and Vincent Nozick and Junichi Yamagishi and Isao Echizen},
journal= {arXiv preprint arXiv:1809.00888},
year = {2021}
}