Towards Generalizable Deepfake Detection by Primary Region
Regularization
- URL: http://arxiv.org/abs/2307.12534v2
- Date: Fri, 28 Jul 2023 10:45:50 GMT
- Title: Towards Generalizable Deepfake Detection by Primary Region
Regularization
- Authors: Harry Cheng and Yangyang Guo and Tianyi Wang and Liqiang Nie and Mohan
Kankanhalli
- Abstract summary: This paper enhances the generalization capability from a novel regularization perspective.
Our method consists of two stages, namely the static localization for primary region maps, and the dynamic exploitation of primary region masks.
We conduct extensive experiments over three widely used deepfake datasets - DFDC, DF-1.0, and Celeb-DF with five backbones.
- Score: 52.41801719896089
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The existing deepfake detection methods have reached a bottleneck in
generalizing to unseen forgeries and manipulation approaches. Based on the
observation that the deepfake detectors exhibit a preference for overfitting
the specific primary regions in input, this paper enhances the generalization
capability from a novel regularization perspective. This can be simply achieved
by augmenting the images through primary region removal, thereby preventing the
detector from over-relying on data bias. Our method consists of two stages,
namely the static localization for primary region maps, as well as the dynamic
exploitation of primary region masks. The proposed method can be seamlessly
integrated into different backbones without affecting their inference
efficiency. We conduct extensive experiments over three widely used deepfake
datasets - DFDC, DF-1.0, and Celeb-DF with five backbones. Our method
demonstrates an average performance improvement of 6% across different
backbones and performs competitively with several state-of-the-art baselines.
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