Toward Calibrated, Fair, and accurate Deepfake Detection
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Computer Science > Machine Learning
Title:Toward Calibrated, Fair, and accurate Deepfake Detection
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.
| Subjects: | Machine Learning (cs.LG); Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2606.09881 [cs.LG] |
| (or arXiv:2606.09881v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.09881
arXiv-issued DOI via DataCite
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