TY - JOUR
AU - Echizen, Isao
AB - 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.
TI - MesoNet: a Compact Facial Video Forgery Detection Network
JF - Computing Research Repository
DO - 10.1109/WIFS.2018.8630761
DA - 2018-09-04
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/mesonet-a-compact-facial-video-forgery-detection-network-PsCoAHW25s
VL - 2021
IS - 1809
DP - DeepDyve
ER -