  Deep neural networks (DNNs) are widely developed and applied in many areas,
and the quality assurance of DNNs is critical. Neural network verification
(NNV) aims to provide formal guarantees to DNN models. Similar to traditional
software, neural network verifiers could also contain bugs, which would have a
critical and serious impact, especially in safety-critical areas. However,
little work exists on validating neural network verifiers. In this work, we
propose DeepGalaxy, an automated approach based on differential testing to
tackle this problem. Specifically, we (1) propose a line of mutation rules,
including model level mutation and specification level mutation, to effectively
explore the two-dimensional input space of neural network verifiers; and (2)
propose heuristic strategies to select test cases. We leveraged our
implementation of DeepGalaxy to test three state-of-the-art neural network
verifies, Marabou, Eran, and Neurify. The experimental results support the
efficiency and effectiveness of DeepGalaxy. Moreover, five unique unknown bugs
were discovered
