Towards Generalizable Deepfake Detection via Real Distribution Bias Correction
To generalize deepfake detectors to future unseen forgeries, most existing methods attempt to simulate the dynamically evolving forgery types using available source domain data. However, predicting an unbounded set of future manipulations from limited prior examples is infeasible. To overcome this limitation, we propose to exploit the invariance of \textbf{real data} from two complementary perspectives: the fixed population distribution of the entire real class and the inherent Gaussianity of individual real images. Building on these properties, we introduce the Real Distribution Bias Correction (RDBC) framework, which consists of two key components: the Real Population Distribution Estim...
| Authors | Ming-Hui Liu, Harry Cheng, Xin Luo et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 March 2026 |
| Last updated at source | 16 September 2026 |
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