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.

翻译：深度伪造检测器在不同人口群体间存在显著的性能差距。现有公平性方法需要人口统计标签、重新训练或牺牲检测精度。我们提出Face-Fairness (FF)——一种即插即用的偏差缓解框架。核心贡献Face-Feature Tuning (FFT)是首个无需人口统计标签的深度伪造检测公平性方法：一种轻量级校准器，基于冻结的人脸嵌入进行逻辑重映射。我们以两种变体补充FFT：FF-Max（在可获取人口统计信息时最大化最差组精度）和FF-Discover（通过嵌入发现的分组实现相同目标）。在领域内与跨数据集测试场景中，FF持续缩小FPR/TPR差距并提升最小分组精度，同时保持（通常提升）整体检测精度。该方法与检测器无关，运行时开销可忽略，且无需访问身份属性。