Paper ID: 2309.16577

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

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

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.

Submitted: Sep 20, 2023