Title: Face-Feature Tuning: Post-hoc Calibration for Fair and Accurate Deepfake Detection
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: deepfake detection, fairness

Abstract: Deepfake detectors often show large performance gaps across demographic groups, undermining trust in deployment. Existing fairness approaches typically require demographic labels, retraining the detector, or accept notable drops in overall accuracy. We introduce Face-Fairness (FF), a plug-and-play framework for mitigating bias in deepfake detection. Our primary contribution, Face-Feature Tuning (FFT), is the first demographic label-free fairness method demonstrated for deepfake detection: a lightweight calibrator that performs post-hoc logit remapping conditioned on frozen face embeddings, trained on a held-out validation split. We complement FFT with two thresholding variants: FF-Max, which maximizes worst-group accuracy when demographics are available on validation, and FF-Discover, which applies the same objective to embedding-discovered groups when demographics are unknown. Across in-domain, cross-protocol, and cross-dataset test settings, FF consistently reduces FPR/TPR gaps and improves minimum group accuracy while maintaining (often improving) overall accuracy. The approach is detector-agnostic, adds negligible runtime overhead, and requires no access to identity attributes—making fairness in deepfake detection practical for real-world systems.

TL;DR: We introduce Face-Feature Tuning (FFT), a post-hoc calibration method that achieves sota fair deepfake detection