SAVER: A Toolbox for SAmpling-Based, Probabilistic VERification of Neural Networks

We present a neural network verification toolbox to 1) assess the probability of satisfaction of a constraint, and 2) modify the set to achieve the probability of satisfaction. Specifically, the tool box establishes with a user-specified level of confidence whether the output of the neural network for a given input distribution is likely to be contained within a given set. Should the tool determine that the given set cannot satisfy the likelihood constraint, the tool also implements an approach outlined in this paper to alter the set to ensure that the user-defined satisfaction probability is achieved. The toolbox is comprised of sampling-based approaches which exploit the properties of signed distance function to define set containment.