Compilation as a Defense: Enhancing DL Model Attack Robustness via Tensor Optimization

Stefan Trawicki; William Hackett; Lewis Birch; Neeraj Suri; Peter Garraghan

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arXiv · 2309.16577

Compilation as a Defense: Enhancing DL Model Attack Robustness via Tensor Optimization

Stefan Trawicki·William Hackett·Lewis Birch·Neeraj Suri·Peter Garraghan

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Abstract

Adversarial Machine Learning (AML) is a rapidly growing field of security research, with an often overlooked area being model attacks through side-channels. Previous works show such attacks to be serious threats, though little progress has been made on efficient remediation strategies that avoid costly model re-engineering. This work demonstrates a new defense against AML side-channel attacks using model compilation techniques, namely tensor optimization. We show relative model attack effectiveness decreases of up to 43% using tensor optimization, discuss the implications, and direction of future work.

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Stefan Trawicki, William Hackett, Lewis Birch, Neeraj Suri, Peter Garraghan. 2023-09-20. Compilation as a Defense: Enhancing DL Model Attack Robustness via Tensor Optimization. https://arxiv.org/abs/2309.16577

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