Adversarial Robustness Through Artifact Design

Adversarial examples arose as a challenge for machine learning. To hinder them, most defenses alter how models are trained (e.g., adversarial training) or inference is made (e.g., randomized smoothing). Still, while these approaches markedly improve models' adversarial robustness, models remain highly susceptible to adversarial examples. Identifying that, in certain domains such as traffic-sign recognition, objects are implemented per standards specifying how artifacts (e.g., signs) should be designed, we propose a novel approach for improving adversarial robustness. Specifically, we offer a …

adversarial adversarial examples adversarial training artifact challenge cs.ai cs.cr cs.cv cs.lg design domains examples hinder inference machine machine learning objects recognition robustness them through traffic training