MesoNet: a Compact Facial Video Forgery Detection Network

Abstract

This paper presents a method to automatically and efficiently detect face\ntampering in videos, and particularly focuses on two recent techniques used to\ngenerate hyper-realistic forged videos: Deepfake and Face2Face. Traditional\nimage forensics techniques are usually not well suited to videos due to the\ncompression that strongly degrades the data. Thus, this paper follows a deep\nlearning approach and presents two networks, both with a low number of layers\nto focus on the mesoscopic properties of images. We evaluate those fast\nnetworks on both an existing dataset and a dataset we have constituted from\nonline videos. The tests demonstrate a very successful detection rate with more\nthan 98% for Deepfake and 95% for Face2Face.\n

Keywords

Computer scienceFocus (optics)Artificial intelligenceFace (sociological concept)Deep learningMesoscopic physicsComputer visionImage (mathematics)

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Publication Info

Year: 2018
Type: preprint
Pages: 1-7
Citations: 1471
Access: Closed

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APA Style

                            
                                    Darius Afchar, 
                                
                                    Vincent Nozick, 
                                
                                    Junichi Yamagishi
                                
                                et al.
                            
                            (2018). 
                            MesoNet: a Compact Facial Video Forgery Detection Network. 
                            2018 IEEE International Workshop on Information Forensics and Security (WIFS)
                            
                            , 1-7.
                            https://doi.org/10.1109/wifs.2018.8630761

Identifiers

DOI: 10.1109/wifs.2018.8630761
arXiv: 1809.00888

Data Quality

Data completeness: 84%