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Scoring Black-Box Models for Adversarial Robustness

arXiv 2210.17140 ↗First posted 31 Oct 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

Deep neural networks are susceptible to adversarial inputs and various methods have been proposed to defend these models against adversarial attacks under different perturbation models. The robustness of models to adversarial attacks has been analyzed by first constructing adversarial inputs for the model, and then testing the model performance on the constructed adversarial inputs. Most of these attacks require the model to be white-box, need access to data labels, and finding adversarial inputs can be computationally expensive. We propose a simple scoring method for black-box models which indicates their robustness to adversarial input. We show that adversarially more robust models have a smaller l1-norm of LIME weights and sharper explanations.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 34 of its 38 references that are in the map. First posted on arXiv 31 Oct 2022.

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References · 34

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22 more
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