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.