Introduces higher-order Langevin dynamics with auxiliary variables as a defense that mixes randomness early to reduce membership inference success on diffusion models, measured via AUROC and FID on toy and speech data.
Are diffusion models vulnerable to membership inference attacks?
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Defending Diffusion Models Against Membership Inference Attacks via Higher-Order Langevin Dynamics
Introduces higher-order Langevin dynamics with auxiliary variables as a defense that mixes randomness early to reduce membership inference success on diffusion models, measured via AUROC and FID on toy and speech data.