2021
DOI: 10.48550/arxiv.2102.05913
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RobOT: Robustness-Oriented Testing for Deep Learning Systems

Abstract: Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, where adversarial examples (a.k.a. bugs) of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the commonly used neuron coverage metrics by existing DL testing approaches are not correlated to model robustness. It is a… Show more

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