Robustness of neural networks to random noise perturbations of their inputs
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Computer Science > Machine Learning
Title:Robustness of neural networks to random noise perturbations of their inputs
Abstract:We investigate the problem of the robustness of a trained neural network to the perturbation of its input values. More specifically, we examine the interplay between the accuracy of the network, as measured by the mean squared error, and robustness. Accordingly, we present a robustness measure, which, with high probability, suggests an upper bound on the mean squared error of the network, with respect to an input data set, for a given perturbation of the input values of the network. The measure we propose is both simple and efficient to compute, treating the neural network as a black box. We provide experimental results on several real-world data sets showing the efficacy of the proposed method. We also introduce the concept of robustness curves, which allows us to further analyse robustness within and between data sets.
| Subjects: | Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2606.31581 [cs.LG] |
| (or arXiv:2606.31581v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.31581
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Martyn Harris Dr [view email][v1] Tue, 30 Jun 2026 12:36:21 UTC (1,624 KB)
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