The increasing reliance on diffusion models for generating synthetic images has amplified concerns about the unauthorized use of personal data, particularly facial images, in model training. In this paper, we introduce a novel identity inference framework to hold model owners accountable for including individuals' identities in their training data. Our approach moves beyond traditional membership inference attacks by focusing on identity-level inference, providing a new perspective on data privacy violations. Through comprehensive evaluations on two facial image datasets, Labeled Faces in the Wild (LFW) and CelebA, our experiments demonstrate that the proposed membership inference attack surpasses baseline methods, achieving an attack success rate of up to 89% and an AUC-ROC of 0.91, while the identity inference attack attains 92% on LDM models trained on LFW, and the data extraction attack achieves 91.6% accuracy on DDPMs, validating the effectiveness of our approach across diffusion models.

本研究针对扩散模型在生成合成图像过程中的个人数据，特别是面部图像的未授权使用问题，提出了一种新的身份推断框架。该框架超越了传统的成员推断攻击，从身份层面对数据隐私侵犯进行了深入探讨，实验结果表明，其攻击成功率高达89%，并在不同模型上取得了显著的有效性验证。

聚焦身份的扩散模型推断和提取攻击