一句话：聚焦神经网络定量鲁棒性验证，旨在不仅判定邻域内是否存在误分类输入，还度量其比例并构建系列工具。
神经网络越来越多用于安全攸关场景，如交通标志识别和医学图像处理，因此对其安全性、正确性以及鲁棒性的验证至关重要。传统鲁棒性验证通常回答给定输入邻域内是否存在被误分类的输入。该研究计划将关注点推进到定量鲁棒性：不仅判断误分类输入是否存在，还要统计其数量，并给出其占邻域大小的比例。整体目标是拓展神经网络的定量鲁棒性验证研究，并开发一系列定量验证工具，以加深对神经网络鲁棒性的理解。摘要未给出具体方法细节与实验结果，属于研究愿景或博士研究计划。
Neural networks are an increasingly common tool for solving problems that require complex analysis and pattern matching, such as identifying stop signs or processing medical imagery. Accordingly, verification of neural networks for safety and correctness is of great importance, as mispredictions can have catastrophic results in safety critical domains. One metric for verification is robustness, which answers whether or not a misclassified input exists in a given input neighborhood. I am focusing my research at quantitative robustness---finding not only if there exist misclassified inputs within a given neighborhood but also how many exist as a proportion of the neighborhood size. My overall goal is to expand the research on quantitative neural network robustness verification and create a variety of quantitative verification tools geared towards expanding our understanding of neural network robustness.