论文标题
物联网边缘设备上的分布式卷积神经网络的安全性如何？
How Secure is Distributed Convolutional Neural Network on IoT Edge Devices?
论文作者
论文摘要
卷积神经网络（CNN）在许多应用中都成功采用了。 CNN在资源受限的边缘设备上的部署已被证明具有挑战性。 CNN在不同边缘设备上的分布式部署已被采用。在本文中，我们提出了特洛伊木马对在不同节点跨分布式边缘网络中部署的CNN的攻击。我们为分布式CNN推断提出了五种隐形攻击方案。这些攻击分为触发和有效载荷电路。这些攻击在深度学习模型（Lenet，Alexnet）上进行了测试。结果表明，各个层的脆弱性程度以及它们对最终分类的关键程度。
Convolutional Neural Networks (CNN) has found successful adoption in many applications. The deployment of CNN on resource-constrained edge devices have proved challenging. CNN distributed deployment across different edge devices has been adopted. In this paper, we propose Trojan attacks on CNN deployed across a distributed edge network across different nodes. We propose five stealthy attack scenarios for distributed CNN inference. These attacks are divided into trigger and payload circuitry. These attacks are tested on deep learning models (LeNet, AlexNet). The results show how the degree of vulnerability of individual layers and how critical they are to the final classification.
