  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.
