# Cost-Sensitive Robustness

Several recent works have developed methods for training classifiers that are certifiably robust against norm-bounded adversarial perturbations. However, these methods assume that all the adversarial transformations provide equal value for adversaries, which is seldom the case in real-world applications.

We advocate for cost-sensitive robustness as the criteria for measuring the classifier’s performance for specific tasks. We encode the potential harm of different adversarial transformations in a cost matrix, and propose a general objective function to adapt the robust training method of Wong & Kolter (2018) to optimize for cost-sensitive robustness. Our experiments on simple MNIST and CIFAR10 models and a variety of cost matrices show that the proposed approach can produce models with substantially reduced cost-sensitive robust error, while maintaining classification accuracy.

### Paper

Xiao Zhang and David Evans. *Cost-Sensitive Robustness against Adversarial Examples*. In *Seventh International Conference on Learning Representations* (ICLR). New Orleans. May 2019. [arXiv] [OpenReview] [PDF]