Adversarial robustness via robust low rank representations

Understand

Adversarial robustness measures the susceptibility of a classifier to imperceptible perturbations made to the inputs at test time.

• In this work we highlight the benefits of natural low rank representations that often exist for real data such as images, for training neural networks with certified robustness guarantees.
• Our first contribution is for certified robustness to perturbations measured in $\ell_2$ norm.
• We exploit low rank data representations to provide improved guarantees over state-of-the-art randomized smoothing-based approaches on standard benchmark datasets such as CIFAR-10 and CIFAR-100.