Difficulty Calibration Across Time for Membership Inference in Diffusion Models

Summary

Researchers address membership inference attacks on diffusion models, which test whether an image was in a model's training set, and note that standard reconstruction-loss scoring fails because some images are inherently easy to reconstruct. They propose difficulty calibration across two time axes: Reference-Model Calibration (RMC) compares a fine-tuned model's loss against an earlier checkpoint, and Temporal Self-Calibration (TSC) compares loss across neighboring diffusion timesteps using a single model. The authors report that their methods outperform prior state of the art by a wide margin and generalize across x-, v-, and ε-prediction models.

Topics

• Training data and privacy

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