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arXiv · 2309.09928

Evaluating Adversarial Robustness with Expected Viable Performance

Ryan McCoppin·Colin Dawson·Sean M. Kennedy·Leslie M. Blaha
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Abstract

We introduce a metric for evaluating the robustness of a classifier, with particular attention to adversarial perturbations, in terms of expected functionality with respect to possible adversarial perturbations. A classifier is assumed to be non-functional (that is, has a functionality of zero) with respect to a perturbation bound if a conventional measure of performance, such as classification accuracy, is less than a minimally viable threshold when the classifier is tested on examples from that perturbation bound. Defining robustness in terms of an expected value is motivated by a domain general approach to robustness quantification.

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Ryan McCoppin, Colin Dawson, Sean M. Kennedy, Leslie M. Blaha. 2023-09-18. Evaluating Adversarial Robustness with Expected Viable Performance. https://arxiv.org/abs/2309.09928

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