SCA Strikes Back

Reverse Engineering Neural Network Architectures using Side Channels

Journal article (2022)

Authors

Lejla Batina Radboud Universiteit Nijmegen

Shivam Bhasin Nanyang Technological University

Dirmanto Jap Nanyang Technological University

S. Picek

DOI: https://doi.org/10.1109/MDAT.2021.3128436

Correlation Neurons Computer architecture Machine learning algorithms Timing Electromagnetics Biological neural networks

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http://resolver.tudelft.nl/uuid:9b832d09-9b7f-424f-affa-f199dbda70df

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Published Date

2022

Language

English

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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.

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