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.

我们提出了一种概率波动评估成员推理攻击（PFAMI），通过分析给定记录周围的整体概率波动趋势来检测这些趋势，从而推断成员身份的黑盒式攻击，实验证明 PFAMI 相对于最佳基线可以提高攻击成功率（ASR）约 27.9%。