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Provably Robust Cost-Sensitive Learning via Randomized Smoothing

Version 2 2024-10-22, 09:24
Version 1 2024-10-15, 13:04
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posted on 2024-10-22, 09:24 authored by Yuan Xin, Michael Backes, Xiao ZhangXiao Zhang
We study the problem of robust learning against adversarial perturbations under cost-sensitive scenarios, where the potential harm of different types of misclassifications is encoded in a cost matrix. Existing approaches are either empirical and cannot certify robustness or suffer from inherent scalability issues. In this work, we investigate whether randomized smoothing, a scalable framework for robustness certification, can be leveraged to certify and train for cost-sensitive robustness. Built upon the notion of cost-sensitive certified radius, we first illustrate how to adapt the standard certification algorithm of randomized smoothing to produce tight robustness certificates for any binary cost matrix, and then develop a robust training method to promote certified cost-sensitive robustness while maintaining the model’s overall accuracy. Through extensive experiments on image benchmarks, we demonstrate the superiority of our proposed certification algorithm and training method under various cost-sensitive scenarios. Our implementation is available as open source code at: https://github.com/TrustMLRG/CS-RS.

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  • Trustworthy Information Processing

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@misc{Xin:Backes:Zhang:2023, title = "Provably Robust Cost-Sensitive Learning via Randomized Smoothing", author = "Xin, Yuan" AND "Backes, Michael" AND "Zhang, Xiao", year = 2023, month = 10 }

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