Jie-Ying Huang

DWAT: Density-Weighted Adversarial Training for Robustness Beyond the Training Perturbation Budget

Deep neural networks (DNNs) are widely deployed in safety-critical applications such as medical diagnosis and autonomous driving. Adversarial training (AT) is among the most effective defenses, casting robust optimization as a min–max problem over a defender-specified ℓp-ball of fixed radius ϵ. Bounded defenses of this...