In-Person Poster presentation / top 25% paper
Certified Training: Small Boxes are All You Need
Mark N Müller · Franziska Eckert · Marc Fischer · Martin Vechev
MH1-2-3-4 #119
Keywords: [ Social Aspects of Machine Learning ] [ Certified Training ] [ certified robustness ] [ Robustness Verification ] [ adversarial robustness ]
To obtain, deterministic guarantees of adversarial robustness, specialized training methods are used. We propose, SABR, a novel such certified training method, based on the key insight that propagating interval bounds for a small but carefully selected subset of the adversarial input region is sufficient to approximate the worst-case loss over the whole region while significantly reducing approximation errors. We show in an extensive empirical evaluation that SABR outperforms existing certified defenses in terms of both standard and certifiable accuracies across perturbation magnitudes and datasets, pointing to a new class of certified training methods promising to alleviate the robustness-accuracy trade-off.