Mitigating Reverse Engineering Attacks on Deep Neural Networks
Yuntao Liu, Dana Dachman-Soled, Ankur Srivastava · 2019
With the structure of deep neural networks (DNN) being of increasing commercial value, DNN reverse engineering attacks have become a great security concern. It has been shown that the memory access pattern of a processor running DNNs can be exploited to decipher their detailed structure. In this work, we propose a defensive memory access mechanism which utilizes oblivious shuffle, address space layout randomization, and dummy memory accesses to counter such attacks. Experiments show that our defense exponentially increases the attack complexity with asymptotically lower memory access overhead compared to generic memory obfuscation techniques such as ORAM and is scalable to larger DNNs.