Towards Certification of Uncertainty Calibration under Adversarial Attacks

• URL: http://arxiv.org/abs/2405.13922v1
• Date: Wed, 22 May 2024 18:52:09 GMT
• Title: Towards Certification of Uncertainty Calibration under Adversarial Attacks
• Authors: Cornelius Emde, Francesco Pinto, Thomas Lukasiewicz, Philip H. S. Torr, Adel Bibi,
• Abstract summary: We show that attacks can significantly harm calibration, and thus propose certified calibration as worst-case bounds on calibration under adversarial perturbations. We propose novel calibration attacks and demonstrate how they can improve model calibration through textitadversarial calibration training
• Score: 96.48317453951418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since neural classifiers are known to be sensitive to adversarial perturbations that alter their accuracy, \textit{certification methods} have been developed to provide provable guarantees on the insensitivity of their predictions to such perturbations. Furthermore, in safety-critical applications, the frequentist interpretation of the confidence of a classifier (also known as model calibration) can be of utmost importance. This property can be measured via the Brier score or the expected calibration error. We show that attacks can significantly harm calibration, and thus propose certified calibration as worst-case bounds on calibration under adversarial perturbations. Specifically, we produce analytic bounds for the Brier score and approximate bounds via the solution of a mixed-integer program on the expected calibration error. Finally, we propose novel calibration attacks and demonstrate how they can improve model calibration through \textit{adversarial calibration training}.

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