Towards Generalizable Deepfake Detection via Real Distribution Bias Correction

Deepfake detection can benefit from the real-image knowledge encoded by pretrained foundation models. Existing approaches seek to retain this knowledge by preserving a subset of pretrained parameters while updating the remaining parameters for deepfake detection. However, freezing pretrained weights cannot freeze their output responses, which can cause channel-parameter mismatch in downstream layers and undermine the use of pretrained knowledge. In principle, gradient-based optimization may mitigate this mismatch, but our analysis reveals that classification supervision overemphasizes a small set of head channels while leaving tail channels weakly constrained. Consequently, the misaligned tail responses can interfere with predictions in unseen domains and hinder generalization. To address this problem, we propose the Real Distribution Bias Correction (RDBC) framework, which uses real-image statistics as reference signals to calibrate the under-constrained channel-response space. Specifically, the Real-Kurtosis-Guided Channel Alignment (RKA) module corrects the accumulated distributional drift at the final normalization layer. Using the input-side kurtosis of real samples as a reference, RKA aligns the output cross-channel response structure and constrains weakly supervised tail-channel responses. The Real-Covariance-Guided Channel Enhancement (RCE) module uses real-feature covariance to propagate the centered deviations of highly responsive channels to other co-varying channels, thereby reducing the reliance on isolated head-channel responses at shallow layers. RCE further samples its statistics from a Gaussian-fitted statistical neighborhood, expanding the coverage of the real-feature reference. Through the complementary calibration of shallow and deep channel responses, RDBC corrects the channel-parameter mismatch while enhancing real-fake separability. Extensive experiments demonstrate that RDBC achieves state-of-the-art performance in cross-domain deepfake detection.