DeepEM: Deep Neural Networks Model Recovery through EM Side-Channel Information Leakage
Honggang Yu, Haocheng Ma, Kaichen Yang, Yiqiang Zhao, Yier Jin
Abstract
Neural Network (NN) accelerators are currently widely deployed in various security-crucial scenarios, including image recognition, natural language processing and autonomous vehicles. Due to economic and privacy concerns, the hardware implementations of structures and designs inside NN accelerators are usually inaccessible to the public. However, these accelerators still tend to leak crucial information through Electromagnetic (EM) side channels in addition to timing and power information. In this paper, we propose an effective and efficient model stealing attack against current popular large-scale NN accelerators deployed on hardware platforms through side-channel information. Specifically, the proposed attack approach contains two stages: 1) Inferring the underlying network architecture through EM sidechannel information; 2) Estimating the parameters, especially the weights, through a margin-based, adversarial active learning method. The experimental results show that the proposed attack approach can accurately recover the large-scale NN through EM side-channel information leakages. Overall, our attack highlights the importance of masking EM traces for large-scale NN accelerators in real-world applications.