AI Research
FYC: FPR-Disparity Weight Perturbation and Youden Calibration for Fair Deepfake Detection
Research targets demographic gaps in deepfake detectors’ false-positive rates
Deepfake detectors routinely achieve high aggregate accuracy while exhibiting substantial demographic disparities in their errors: false-positive rates differ by double digits across gender and skin tone.
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ArXiv CS.CV
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2 min
ArXiv CS.CV
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Generated insight by OnAirToday AI
High aggregate accuracy can hide substantial differences in false-positive rates across demographic groups. The research focuses on weight perturbation and Youden calibration as approaches to addressing disparities in deepfake detection.
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