While Deep Neural Networks (DNNs) have demonstrated remarkable performance in
tasks related to perception and control, there are still several unresolved
concerns regarding the privacy of their training data, particularly in the
context of vulnerability to Membership Inference Attacks (MIAs). In this paper,
we explore a connection between the susceptibility to membership inference
attacks and the vulnerability to distillation-based functionality stealing
attacks. In particular, we propose {GLiRA}, a distillation-guided approach to
membership inference attack on the black-box neural network. We observe that
the knowledge distillation significantly improves the efficiency of likelihood
ratio of membership inference attack, especially in the black-box setting,
i.e., when the architecture of the target model is unknown to the attacker. We
evaluate the proposed method across multiple image classification datasets and
models and demonstrate that likelihood ratio attacks when guided by the
knowledge distillation, outperform the current state-of-the-art membership
inference attacks in the black-box setting.

我们提出了一种使用知识蒸馏引导的黑盒神经网络成员推理攻击方法，并在多个图像分类数据集和模型上进行评估，证明了借助知识蒸馏的似然比攻击在黑盒设置下优于目前最先进的成员推理攻击。