Kernel-based distributional discrepancy enables auditing of upstream training data in distilled one-step diffusion models by detecting preserved distributional alignment rather than per-instance memorization.
MMD and related techniques have been extensively adopted in real-world applications, including healthcare (Guo et al., 2022; Jiang et al., 2016; Zhong et al.,
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Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models
Kernel-based distributional discrepancy enables auditing of upstream training data in distilled one-step diffusion models by detecting preserved distributional alignment rather than per-instance memorization.