  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.
