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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AMRL reaches state-of-the-art accuracy on apparent age estimation yet exhibits clear performance drops for Asian and African American groups due to inconsistent feature focus, showing that technical tweaks are not enough without diverse localized data.
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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Apparent Age Estimation: Challenges and Outcomes
AMRL reaches state-of-the-art accuracy on apparent age estimation yet exhibits clear performance drops for Asian and African American groups due to inconsistent feature focus, showing that technical tweaks are not enough without diverse localized data.