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Delocate: Detection and Localization for Deepfake Videos with Randomly-Located Tampered Traces
April 23, 2024, 4:48 a.m. | Juan Hu, Xin Liao, Difei Gao, Satoshi Tsutsui, Qian Wang, Zheng Qin, Mike Zheng Shou
cs.CV updates on arXiv.org arxiv.org
Abstract: 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 …
abstract arxiv cs.cr cs.cv deepfake detection detection methods domain localization novel struggle traces type videos
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