  Formal verification has emerged as a powerful approach to ensure the safety
and reliability of deep neural networks. However, current verification tools
are limited to only a handful of properties that can be expressed as
first-order constraints over the inputs and output of a network. While
adversarial robustness and fairness fall under this category, many real-world
properties (e.g., "an autonomous vehicle has to stop in front of a stop sign")
remain outside the scope of existing verification technology. To mitigate this
severe practical restriction, we introduce a novel framework for verifying
neural networks, named neuro-symbolic verification. The key idea is to use
neural networks as part of the otherwise logical specification, enabling the
verification of a wide variety of complex, real-world properties, including the
one above. Moreover, we demonstrate how neuro-symbolic verification can be
implemented on top of existing verification infrastructure for neural networks,
making our framework easily accessible to researchers and practitioners alike.
