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Neuro-Symbolic Verification of Deep Neural Networks

Xuan Xie; Kristian Kersting; Daniel Neider
In: Luc De Raedt (Hrsg.). Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. International Joint Conference on Artificial Intelligence (IJCAI-2022), July 23-29, Vienna, Austria, Pages 3622-3628,, 2022.


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.

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