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Computer Science > Computer Vision and Pattern Recognition

arXiv:2406.09739 (cs)
[Submitted on 14 Jun 2024 (v1), last revised 19 Aug 2024 (this version, v2)]

Title:Decoupling Forgery Semantics for Generalizable Deepfake Detection

Authors:Wei Ye, Xinan He, Feng Ding
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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: this https URL.
Comments: Accepted by BMVC 2024
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2406.09739 [cs.CV]
  (or arXiv:2406.09739v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2406.09739

Submission history

From: Wei Ye [view email]
[v1] Fri, 14 Jun 2024 06:00:14 UTC (12,546 KB)
[v2] Mon, 19 Aug 2024 06:27:49 UTC (12,546 KB)
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