IoT边缘设备上的分布式卷积神经网络有多安全？
How Secure is Distributed Convolutional Neural Network on IoT Edge Devices?
摘要：
卷积神经网络（CNN）已在许多应用中获得成功。在资源受限的边缘设备上部署CNN已被证明具有挑战性。采用了跨不同边缘设备的CNN分布式部署。在本文中，我们提出了木马攻击CNN部署在一个分布式的边缘网络在不同的节点。我们提出了五种分布式CNN推理的隐形攻击场景。这些攻击分为触发电路和有效载荷电路。这些攻击在深度学习模型（LeNet，AlexNet）上进行了测试。结果显示了各个层的脆弱性程度以及它们对最终分类的重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
