SCA Strikes Back
Reverse Engineering Neural Network Architectures using Side Channels
Lejla Batina (Radboud Universiteit Nijmegen)
Shivam Bhasin (Nanyang Technological University)
Dirmanto Jap (Nanyang Technological University)
Stjepan Picek (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
This paper was selected for Top Picks in Hardware and Embedded Security 2020 and it presents a physical side-channel attack aiming at reverse engineering neural networks implemented on an edge device. The attack does not need access to training data and allows for neural network recovery by feeding known random inputs. We successfully reverse engineer information about layers, neurons, activation functions, and weights associated with neurons. This attack opens a new door in the domain of security of neural networks. Follow-up works by other researchers have shown this attack to be applicable for various settings and difficult to protect against.