Al Makdah, AA and Katewa, V and Pasqualetti, F (2021) Robust Adversarial Classification via Abstaining. In: 2021 60th IEEE Conference on Decision and Control (CDC), 14-17 Dec. 2021, Austin, TX, USA, pp. 763-768.
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Abstract
In this work, we consider a binary classification problem and cast it into a binary hypothesis testing framework, where the observations can be perturbed by an adversary. To improve the adversarial robustness of a classifier, we include an abstain option, where the classifier abstains from making a decision when it has low confidence about the prediction. We propose metrics to quantify the nominal performance of a classifier with an abstain option and its robustness against adversarial perturbations. We show that there exist a tradeoff between the two metrics regardless of what method is used to choose the abstain region. Our results imply that the robustness of a classifier with an abstain option can only be improved at the expense of its nominal performance. Further, we provide necessary conditions to design the abstain region for a 1-dimensional binary classification problem. We validate our theoretical results on the MNIST dataset, where we numerically show that the tradeoff between performance and robustness also exist for the general multi-class classification problems. © 2021 IEEE.
| Item Type: | Conference Proceedings |
|---|---|
| Publication: | Proceedings of the IEEE Conference on Decision and Control |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to the authors. |
| Keywords: | Computer vision, Adversarial classifications; Binary classification problems; Binary Hypothesis Testing; Condition; Multiclass classification problems; Performance; Testing framework, Classification (of information) |
| Department/Centre: | Division of Electrical Sciences > Electrical Communication Engineering |
| Date Deposited: | 15 May 2022 17:00 |
| Last Modified: | 15 May 2022 17:00 |
| URI: | https://eprints.iisc.ac.in/id/eprint/71662 |
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