Memorization in diffusion models can be reliably detected by comparing reconstruction losses across time (training or diffusion timesteps) rather than using absolute loss values, which conflate memorization with image difficulty.
This paper tackles membership inference attacks on diffusion models by separating true memorization from inherent image difficulty. The authors propose two calibration methods: Reference-Model Calibration (RMC) compares losses across training checkpoints, while Temporal Self-Calibration (TSC) uses a single model by comparing losses at different noise levels.