Evaluating Adversarial Robustness with Expected Viable Performance

• URL: http://arxiv.org/abs/2309.09928v1
• Date: Mon, 18 Sep 2023 16:47:24 GMT
• Title: Evaluating Adversarial Robustness with Expected Viable Performance
• Authors: Ryan McCoppin, Colin Dawson, Sean M. Kennedy, Leslie M. Blaha
• Abstract summary: We introduce a metric for evaluating the robustness of a classifier, with particular attention to 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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