Towards Generalizable Deepfake Detection via Real Distribution Bias Correction

TL;DR

This paper introduces a novel deepfake detection method that leverages the invariant properties of real data, such as population distribution and Gaussianity, to improve generalization to unseen forgeries.

Contribution

The proposed RDBC framework exploits real data invariances to enhance deepfake detector generalization, addressing limitations of prior methods that rely on simulating future forgery types.

Findings

Achieves state-of-the-art results in cross-domain deepfake detection.

Effectively captures real-world properties of real samples.

Improves generalization to unseen forgeries.

Abstract

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 Estimation module and the Distribution-Sampled Feature Whitening module. The former utilizes the independent and identically distributed (\iid) property of real samples to derive the normal distribution form of their statistics, from which the distribution parameters can be estimated using limited source domain data. Based on the learned population distribution, the latter utilizes the inherent Gaussianity of real data as a discriminative prior and performs a sampling-based whitening operation to amplify the Gaussianity gap between real and fake samples. Through synergistic coupling of the two modules, our model captures the real-world properties of real samples, thereby enhancing its generalizability to unseen target domains. Extensive experiments demonstrate that RDBC achieves state-of-the-art performance in both in-domain and cross-domain deepfake detection.