Certified Robust Models with Slack Control and Large Lipschitz Constants

Understand

Despite recent success, state-of-the-art learning-based models remain highly vulnerable to input changes such as adversarial examples.

• In order to obtain certifiable robustness against such perturbations, recent work considers Lipschitz-based regularizers or constraints while at the same time increasing prediction margin.
• Unfortunately, this comes at the cost of significantly decreased accuracy.
• In this paper, we propose a Calibrated Lipschitz-Margin Loss (CLL) that addresses this issue and improves certified robustness by tackling two problems: Firstly, commonly used margin losses do not adjust the penalties to the shrinking output distribution; caused by minimizing the Lipschitz constant $K$.