论文标题
对数字双胞胎的白盒对抗性攻击
A White-Box Adversarial Attack Against a Digital Twin
论文作者
论文摘要
最近的研究表明，机器学习/深度学习（ML/DL）模型特别容易受到对抗性扰动的影响，这是对输入数据进行的小更改，以欺骗机器学习分类器。数字双胞胎通常被描述为由物理实体，虚拟对应物以及介于两者之间的数据连接组成，越来越多地被调查，作为通过利用计算技术来改善物理实体的性能的手段，该计算技术由虚拟对应物启用。本文探讨了Digital Twin（DT）的敏感性，Digital Twin（DT）是一种虚拟模型，该虚拟模型旨在使用ML/DL分类器准确反映物理对象，该对象可以用作网络物理系统（CPS）来对抗对抗性攻击。作为概念证明，我们首先使用深层神经网络体系结构制定了车辆系统的DT，然后利用它来发起对抗性攻击。我们通过扰动训练有素的模型的输入来攻击DT模型，并显示模型如何通过白框攻击轻松破坏。
Recent research has shown that Machine Learning/Deep Learning (ML/DL) models are particularly vulnerable to adversarial perturbations, which are small changes made to the input data in order to fool a machine learning classifier. The Digital Twin, which is typically described as consisting of a physical entity, a virtual counterpart, and the data connections in between, is increasingly being investigated as a means of improving the performance of physical entities by leveraging computational techniques, which are enabled by the virtual counterpart. This paper explores the susceptibility of Digital Twin (DT), a virtual model designed to accurately reflect a physical object using ML/DL classifiers that operate as Cyber Physical Systems (CPS), to adversarial attacks. As a proof of concept, we first formulate a DT of a vehicular system using a deep neural network architecture and then utilize it to launch an adversarial attack. We attack the DT model by perturbing the input to the trained model and show how easily the model can be broken with white-box attacks.
