Provably Robust Cost-Sensitive Learning via Randomized Smoothing
Yuan Xin Michael Backes Xiao Zhang
We focus on learning adversarially robust classifiers under a cost-sensitive
scenario, where the potential harm of different classwise adversarial
transformations is encoded in a binary cost matrix. Existing methods are either
empirical that cannot certify robustness or suffer from inherent scalability
issues. In this work, we study whether randomized smoothing, a more scalable
robustness certification framework, can be leveraged to certify cost-sensitive
robustness. Built upon a notion of cost-sensitive certified radius, we show how
to adapt the standard randomized smoothing certification pipeline to produce
tight robustness guarantees for any cost matrix. In addition, with fine-grained
certified radius optimization schemes specifically designed for different data
subgroups, we propose an algorithm to train smoothed classifiers that are
optimized for cost-sensitive robustness. Extensive experiments on image
benchmarks and a real-world medical dataset demonstrate the superiority of our
method in achieving significantly improved performance of certified
cost-sensitive robustness while having a negligible impact on overall accuracy.