Verifying And Interpreting Neural Networks using Finite Automata

• URL: http://arxiv.org/abs/2211.01022v3
• Date: Wed, 27 Sep 2023 03:37:39 GMT
• Title: Verifying And Interpreting Neural Networks using Finite Automata
• Authors: Marco S\"alzer, Eric Alsmann, Florian Bruse and Martin Lange
• Abstract summary: We propose an automata-theoric approach to tackling problems arising in DNN analysis. We show that the input-output behaviour of a DNN can be captured precisely by a weak B"uchi automaton.
• Score: 2.048226951354646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Verifying properties and interpreting the behaviour of deep neural networks (DNN) is an important task given their ubiquitous use in applications, including safety-critical ones, and their black-box nature. We propose an automata-theoric approach to tackling problems arising in DNN analysis. We show that the input-output behaviour of a DNN can be captured precisely by a (special) weak B\"uchi automaton and we show how these can be used to address common verification and interpretation tasks of DNN like adversarial robustness or minimum sufficient reasons.

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