论文标题
用户级会员资格推理攻击针对公制嵌入学习
User-Level Membership Inference Attack against Metric Embedding Learning
论文作者
论文摘要
会员推理（MI）确定样本是否是受害者模型培训集的一部分。 MI攻击的最新发展集中在记录级成员推理上，这在许多实际情况下限制了其应用程序。例如，在人员重新识别任务中，攻击者（或调查人员）有兴趣确定在培训期间是否使用了用户的图像。但是，攻击者可能无法访问确切的训练图像。在本文中，我们开发了一个用户级的MI攻击，即使在攻击者没有确切的训练样本中，在训练过程中是否使用了目标用户的任何样本。我们专注于指标嵌入学习，因为它在人的重新识别中的主导地位，在这种情况下，用户级的MI攻击更为明智。我们对几个数据集进行了广泛的评估，并表明我们的方法在用户级MI任务上实现了很高的准确性。
Membership inference (MI) determines if a sample was part of a victim model training set. Recent development of MI attacks focus on record-level membership inference which limits their application in many real-world scenarios. For example, in the person re-identification task, the attacker (or investigator) is interested in determining if a user's images have been used during training or not. However, the exact training images might not be accessible to the attacker. In this paper, we develop a user-level MI attack where the goal is to find if any sample from the target user has been used during training even when no exact training sample is available to the attacker. We focus on metric embedding learning due to its dominance in person re-identification, where user-level MI attack is more sensible. We conduct an extensive evaluation on several datasets and show that our approach achieves high accuracy on user-level MI task.
