Toward Calibrated, Fair, and accurate Deepfake Detection

# Toward Calibrated, Fair, and accurate Deepfake Detection
Source: [https://arxiv.org/abs/2606.09881](https://arxiv.org/abs/2606.09881)
[View PDF](https://arxiv.org/pdf/2606.09881)

> Abstract:Deepfake detectors show large performance gaps across demographic groups\. Existing fairness approaches require demographic labels, retraining, or sacrifice accuracy\. We introduce Face\-Fairness \(FF\), a plug\-and\-play framework for bias mitigation\. Our primary contribution, Face\-Feature Tuning \(FFT\), is the first demographic label\-free fairness method demonstrated for deepfake detection: a lightweight calibrator that performs a logit remapping conditioned on frozen face embeddings\. We complement FFT with two variants: FF\-Max, which maximizes worst\-group accuracy when demographics are available, and FF\-Discover, which does the same with embedding\-discovered groups\. Across in\-domain 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\.

## Submission history

From: Ryan Brown \[[view email](https://arxiv.org/show-email/f8990f23/2606.09881)\] **\[v1\]**Wed, 3 Jun 2026 05:44:29 UTC \(407 KB\)