Robust Adversarial Defense by Tensor Factorization
Manish Bhattarai Mehmet Cagri Kaymak Ryan Barron Ben Nebgen Kim Rasmussen Boian Alexandrov
As machine learning techniques become increasingly prevalent in data
analysis, the threat of adversarial attacks has surged, necessitating robust
defense mechanisms. Among these defenses, methods exploiting low-rank
approximations for input data preprocessing and neural network (NN) parameter
factorization have shown potential. Our work advances this field further by
integrating the tensorization of input data with low-rank decomposition and
tensorization of NN parameters to enhance adversarial defense. The proposed
approach demonstrates significant defense capabilities, maintaining robust
accuracy even when subjected to the strongest known auto-attacks. Evaluations
against leading-edge robust performance benchmarks reveal that our results not
only hold their ground against the best defensive methods available but also
exceed all current defense strategies that rely on tensor factorizations. This
study underscores the potential of integrating tensorization and low-rank
decomposition as a robust defense against adversarial attacks in machine
learning.