Reverse engineering convolutional neural networks through side-channel information leaks

Weizhe Hua, Zhiru Zhang, G. Edward Suh

Abstract: A convolutional neural network (CNN) model represents a crucial piece of intellectual property in many applications. Revealing its structure or weights would leak confidential information. In this paper we present novel reverse-engineering attacks on CNNs running on a hardware accelerator, where an adversary can feed inputs to the accelerator and observe the resulting off-chip memory accesses. Our study shows that even with data encryption, the adversary can infer the underlying network structure by exploiting the memory and timing side-channels. We further identify the information leakage on the values of weights when a CNN accelerator performs dynamic zero pruning for off-chip memory accesses. Overall, this work reveals the importance of hiding off-chip memory access pattern to truly protect confidential CNN models.