Recently, diffusion models have become popular tools for image synthesis because of their high-quality outputs. However, like other large-scale models, they may leak private information about their training data. Here, we demonstrate a privacy vulnerability of diffusion models through a \emph{membership inference (MI) attack}, which aims to identify whether a target example belongs to the training set when given the trained diffusion model. Our proposed MI attack learns quantile regression models that predict (a quantile of) the distribution of reconstruction loss on examples not used in training. This allows us to define a granular hypothesis test for determining the membership of a point in the training set, based on thresholding the reconstruction loss of that point using a custom threshold tailored to the example. We also provide a simple bootstrap technique that takes a majority membership prediction over ``a bag of weak attackers'' which improves the accuracy over individual quantile regression models. We show that our attack outperforms the prior state-of-the-art attack while being substantially less computationally expensive -- prior attacks required training multiple ``shadow models'' with the same architecture as the model under attack, whereas our attack requires training only much smaller models.

扩散模型具有高质量输出，但容易泄漏训练数据的具体信息，我们提出了成员推断攻击一文，通过量化回归模型预测训练集以外示例的重构损失分布，并使用自定义阈值在重构损失上进行假设检验，最终确定数据点是否属于训练集。我们还提供了简单的引导技术，通过多个弱攻击者的多数成员预测来提高准确性，相较于以往的攻击方法而言，我们的攻击方法计算成本低得多。

通过分位数回归对扩散模型进行的成员推断攻击