论文标题
通过利用策略网络信息，一种针对动态神经网络的新型会员推断攻击
A Novel Membership Inference Attack against Dynamic Neural Networks by Utilizing Policy Networks Information
论文作者
论文摘要
与传统的静态深神经网络（DNN）不同，动态神经网络（NNS）将其结构或参数调整为不同的输入，以确保准确性和计算效率。同时，这是最近深度学习的新兴研究领域。尽管传统的静态DNN容易受到会员推理攻击（MIA）的攻击，该攻击旨在推断是否使用特定点来训练模型，但对这种攻击在动态NNS上的表现知之甚少。在本文中，我们提出了针对动态NNS的新型MI攻击，利用动态NNS的独特政策网络来提高成员推理的有效性。我们使用两个动态NN，即Gaternet，Blockdrop进行了广泛的实验，在四个主流图像分类任务上，即CIFAR-10，CIFAR-100，STL-10，STL-10和GTSRB。评估结果表明，控制流信息可以显着促进MIA。基于骨干 - 通讯和信息融合，我们的方法比使用中间信息获得了基线攻击和传统攻击更好的结果。
Unlike traditional static deep neural networks (DNNs), dynamic neural networks (NNs) adjust their structures or parameters to different inputs to guarantee accuracy and computational efficiency. Meanwhile, it has been an emerging research area in deep learning recently. Although traditional static DNNs are vulnerable to the membership inference attack (MIA) , which aims to infer whether a particular point was used to train the model, little is known about how such an attack performs on the dynamic NNs. In this paper, we propose a novel MI attack against dynamic NNs, leveraging the unique policy networks mechanism of dynamic NNs to increase the effectiveness of membership inference. We conducted extensive experiments using two dynamic NNs, i.e., GaterNet, BlockDrop, on four mainstream image classification tasks, i.e., CIFAR-10, CIFAR-100, STL-10, and GTSRB. The evaluation results demonstrate that the control-flow information can significantly promote the MIA. Based on backbone-finetuning and information-fusion, our method achieves better results than baseline attack and traditional attack using intermediate information.
