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Article Dans Une Revue International Journal on Software Tools for Technology Transfer Année : 2023

Analysis of recurrent neural networks via property-directed verification of surrogate models

Résumé

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 toward 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.
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Dates et versions

hal-04286080 , version 1 (15-11-2023)

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Igor Khmelnitsky, Daniel Neider, Rajarshi Roy, Xuan Xie, Benoît Barbot, et al.. Analysis of recurrent neural networks via property-directed verification of surrogate models. International Journal on Software Tools for Technology Transfer, 2023, 25 (3), pp.341-354. ⟨10.1007/S10009-022-00684-W⟩. ⟨hal-04286080⟩
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