Jan. 25, 2024, 2:10 a.m. | Juan Hu, Xin Liao, Difei Gao, Satoshi Tsutsui, Qian Wang, Zheng Qin, Mike Zheng Shou

cs.CR updates on arXiv.org arxiv.org

Deepfake videos are becoming increasingly realistic, showing subtle tampering
traces on facial areasthat vary between frames. Consequently, many existing
Deepfake detection methods struggle to detect unknown domain Deepfake videos
while accurately locating the tampered region. To address thislimitation, we
propose Delocate, a novel Deepfake detection model that can both recognize
andlocalize unknown domain Deepfake videos. Ourmethod consists of two stages
named recoveringand localization. In the recovering stage, the modelrandomly
masks regions of interest (ROIs) and reconstructs real faces without tampering …

address arxiv deepfake deepfake detection deepfake videos detect detection domain facial localization novel tampering traces videos

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