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

Explore cutting-edge research on enhancing DL model attack robustness through tensor optimization. Learn about AML side-channel defense strategies and extraction attacks on Deep Learning models.

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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