Membership Inference Attack (MIA) identifies whether a record exists in a machine learning model's training set by querying the model. MIAs on the classic classification models have been well-studied, and recent works have started to explore how to transplant MIA onto generative models. Our investigation indicates that existing MIAs designed for generative models mainly depend on the overfitting in target models. However, overfitting can be avoided by employing various regularization techniques, whereas existing MIAs demonstrate poor performance in practice. Unlike overfitting, memorization is essential for deep learning models to attain optimal performance, making it a more prevalent phenomenon. Memorization in generative models leads to an increasing trend in the probability distribution of generating records around the member record. Therefore, we propose a Probabilistic Fluctuation Assessing Membership Inference Attack (PFAMI), a black-box MIA that infers memberships by detecting these trends via analyzing the overall probabilistic fluctuations around given records. We conduct extensive experiments across multiple generative models and datasets, which demonstrate PFAMI can improve the attack success rate (ASR) by about 27.9% when compared with the best baseline.

翻译：成员推理攻击（MIA）通过查询模型来判断某条记录是否存在于机器学习模型的训练集中。针对经典分类模型的MIA已得到充分研究，而近期工作开始探索如何将MIA移植到生成模型上。我们的研究表明，现有针对生成模型的MIA主要依赖于目标模型的过拟合现象。然而，通过采用各种正则化技术可以避免过拟合，导致现有MIA在实践中表现不佳。与过拟合不同，记忆化是深度学习模型达到最优性能的必要条件，因此是一种更普遍的现象。生成模型中的记忆化会导致生成记录在成员记录周围的概率分布呈现上升趋势。为此，我们提出了一种概率波动评估成员推理攻击（PFAMI），这是一种黑盒MIA方法，通过分析给定记录周围的整体概率波动来检测这些趋势从而推断成员关系。我们在多个生成模型和数据集上开展了广泛实验，结果表明，与最佳基线相比，PFAMI可将攻击成功率（ASR）提升约27.9%。