# RoMA: a Method for Neural Network Robustness Measurement and Assessment

@article{Levy2021RoMAAM, title={RoMA: a Method for Neural Network Robustness Measurement and Assessment}, author={Natan Levy and Guy Katz}, journal={ArXiv}, year={2021}, volume={abs/2110.11088} }

Neural network models have become the leading solution for a large variety of tasks, such as classification, language processing, protein folding, and others. However, their reliability is heavily plagued by adversarial inputs: small input perturbations that cause the model to produce erroneous outputs. Adversarial inputs can occur naturally when the system’s environment behaves randomly, even in the absence of a malicious adversary, and are a severe cause for concern when attempting to deploy…

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