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MesoNet: a Compact Facial Video Forgery Detection Network

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.
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Contributor : Vincent Nozick Connect in order to contact the contributor
Submitted on : Tuesday, September 4, 2018 - 11:15:02 AM
Last modification on : Saturday, December 4, 2021 - 3:58:29 AM
Long-term archiving on: : Wednesday, December 5, 2018 - 2:44:57 PM


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  • HAL Id : hal-01867298, version 1
  • ARXIV : 1809.00888


Darius Afchar, Vincent Nozick, Junichi Yamagishi, Isao Echizen. MesoNet: a Compact Facial Video Forgery Detection Network. 2018. ⟨hal-01867298⟩



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