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Erschienen in:
2010 | OriginalPaper | Buchkapitel
An Abstraction-Refinement Approach to Verification of Artificial Neural Networks
verfasst von : Luca Pulina, Armando Tacchella
Erschienen in: Computer Aided Verification
Verlag: Springer Berlin Heidelberg
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A key problem in the adoption of artificial neural networks in safety-related applications is that misbehaviors can be hardly ruled out with traditional analytical or probabilistic techniques. In this paper we focus on specific networks known as Multi-Layer Perceptrons (MLPs), and we propose a solution to verify their safety using abstractions to Boolean combinations of linear arithmetic constraints. We show that our abstractions are consistent, i.e., whenever the abstract MLP is declared to be safe, the same holds for the concrete one. Spurious counterexamples, on the other hand, trigger refinements and can be leveraged to automate the correction of misbehaviors. We describe an implementation of our approach based on the
HySAT
solver, detailing the abstraction-refinement process and the automated correction strategy. Finally, we present experimental results confirming the feasibility of our approach on a realistic case study.