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Difficulty Calibration Across Time for Membership Inference in Diffusion Models

Paper recorded by Signals 4 on 2026-10-29 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-29 on arXiv · recorded by Signals 4 on 2026-10-30

Category: cs.LG · 机器学习 · first seen 2026-10-30

Abstract

Modern diffusion models are trained on massive datasets that often include copyrighted or private images. Membership inference attacks (MIAs) address this by testing whether a given image was in a model's training set. The standard approach scores an image by its reconstruction loss and flags low-loss images as members. This often fails because some images are simply easier to reconstruct than oth

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#71 most recent of 703 cs.LG papers we have recorded · ↑ newer: An ODE-based Sampler for Discrete Diffusion Models · ↓ older: Training Foundation Models with a Single Algorithm
Cite this page: Difficulty Calibration Across Time for Membership Inference in Diffusion Models: the #71 most recent of 703 cs.LG papers we have recorded (as of 2026-10-29). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/difficulty-calibration-across-time-for-membership-inference-in-diffusion-models.html
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