  The success of neural networks across most machine learning tasks and the
persistence of adversarial examples have made the verification of such models
an important quest. Several techniques have been successfully developed to
verify robustness, and are now able to evaluate neural networks with thousands
of nodes. The main weakness of this approach is in the specification:
robustness is asserted on a validation set consisting of a finite set of
examples, i.e. locally.
  We propose a notion of global robustness based on generative models, which
asserts the robustness on a very large and representative set of examples. We
show how this can be used for verifying neural networks. In this paper we
experimentally explore the merits of this approach, and show how it can be used
to construct realistic adversarial examples.
