Abstract
This paper presents a property-directed approach to verifying recurrent neural networks (RNNs). To this end, we learn a deterministic finite automaton as a surrogate model from a given RNN using active automata learning. This model may then be analyzed using model checking as a verification technique. The term property-directed reflects the idea that our procedure is guided and controlled by the given property rather than performing the two steps separately. We show that this not only allows us to discover small counterexamples fast, but also to generalize them by pumping towards faulty flows hinting at the underlying error in the RNN. We also show that our method can be efficiently used for adversarial robustness certification of RNNs.
The first four authors contributed equally, the remaining authors are ordered alphabetically. This work was partly supported by the PHC PROCOPE 2020 project LeaRNNify (number 44707TK), funded by DAAD and Campus France and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) grant number 434592664.
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Notes
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In the index of the right congruence associated with L and in the size of the longest counterexample obtained as a reply to an EQ.
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Khmelnitsky, I. et al. (2021). Property-Directed Verification and Robustness Certification of Recurrent Neural Networks. In: Hou, Z., Ganesh, V. (eds) Automated Technology for Verification and Analysis. ATVA 2021. Lecture Notes in Computer Science(), vol 12971. Springer, Cham. https://doi.org/10.1007/978-3-030-88885-5_24
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