Evaluating Adversarial Robustness with Expected Viable Performance
Machine Learning
2023-09-19 v1
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.
Keywords
Cite
@article{arxiv.2309.09928,
title = {Evaluating Adversarial Robustness with Expected Viable Performance},
author = {Ryan McCoppin and Colin Dawson and Sean M. Kennedy and Leslie M. Blaha},
journal= {arXiv preprint arXiv:2309.09928},
year = {2023}
}
Comments
Accepted at the 22nd International Conference on Machine Learning and Applications (IEEE 2023)