Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
An examination of fairness of AI models for deepfake detection
3 Pith papers cite this work, alongside 6 external citations. Polarity classification is still indexing.
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Pith papers citing it
6
external citations · external index
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2026 3representative citing papers
A systematic audit of 155 face swap apps finds 70% lack safeguards against generating non-consensual nude imagery.
Fairness metrics uncover gender disparities in audio deepfake detection error distributions that standard Equal Error Rate metrics obscure.
citing papers explorer
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Toward Calibrated, Fair, and accurate Deepfake Detection
Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
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Dual-Use AI Face Swap Apps Are Mostly Unsafe: A Systematic Safety Audit
A systematic audit of 155 face swap apps finds 70% lack safeguards against generating non-consensual nude imagery.
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Gender Fairness in Audio Deepfake Detection: Performance and Disparity Analysis
Fairness metrics uncover gender disparities in audio deepfake detection error distributions that standard Equal Error Rate metrics obscure.