CheckINN: wide range neural network verification in imandra

Desmartin, Remi, Passmore, Grant, Komendantskaya, Ekaterina and Daggit, Matthew (2022) CheckINN: wide range neural network verification in imandra. In Proceedings of the 24th International Symposium on Principles and Practice of Declarative Programming, PPDP 2022 - Co-located with CLAS 2022 (including LOPSTR 2022). Association for Computing Machinery.. (doi:10.1145/3551357.3551372).

Abstract

Neural networks are increasingly relied upon as components of complex safety-critical systems such as autonomous vehicles. There is high demand for tools and methods that embed neural network verification in a larger verification cycle. However, neural network verification is difficult due to a wide range of verification properties of interest, each typically only amenable to verification in specialised solvers. In this paper, we show how Imandra, a functional programming language and a theorem prover originally designed for verification, validation and simulation of financial infrastructure can offer a holistic infrastructure for neural network verification. We develop a novel library CheckINN that formalises neural networks in Imandra, and covers different important facets of neural network verification.

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