Quantitative Robustness Analysis of Neural Networks

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.