Decoupling Forgery Semantics for Generalizable Deepfake Detection


Wei Ye (Nanchang University), Xinan He (Nanchang University), Feng Ding (Nanchang University)
The 35th British Machine Vision Conference
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Abstract

In this paper, we propose a novel method for detecting DeepFakes, enhancing the generalization of detection through semantic decoupling. There are now multiple DeepFake forgery technologies that not only possess unique forgery semantics but may also share common forgery semantics. The unique forgery semantics and irrelevant content semantics may promote over-fitting and hamper generalization for DeepFake detectors. For our proposed method, after decoupling, the common forgery semantics could be extracted from DeepFakes, and subsequently be employed for developing the generalizability of DeepFake detectors. Also, to pursue additional generalizability, we designed an adaptive high-pass module and a two-stage training strategy to improve the independence of decoupled semantics. Evaluation on FF++, Celeb-DF, DFD, and DFDC datasets showcases our method's excellent detection and generalization performance. Code is available at: https://github.com/leaffeall/DFS-GDD.

Citation

@inproceedings{Ye_2024_BMVC,
author    = {Wei Ye and Xinan He and Feng Ding},
title     = {Decoupling Forgery Semantics for Generalizable Deepfake Detection},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0401.pdf}
}


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