Deep learning

Physics

Sociology

Social science

Image (mathematics)

Optics

Artificial intelligence

Mesoscopic physics

Focus (optics)

Face (sociological concept)

Computer science

Quantum mechanics

Computer vision

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.

MesoNet: a Compact Facial Video Forgery Detection Network