MesoNet: a Compact Facial Video Forgery Detection Network

TL;DR

This paper introduces MesoNet, a lightweight deep learning model designed to efficiently detect face tampering in videos, achieving over 98% accuracy on Deepfake and Face2Face forgeries.

Contribution

It proposes two low-layer networks focusing on mesoscopic image features, tailored for fast and effective video forgery detection.

Findings

Over 98% detection accuracy for Deepfake videos

Over 95% detection accuracy for Face2Face videos

Effective on both existing and newly collected datasets

Abstract

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.