Automated Safety Verification of Programs Invoking Neural Networks
Author:
Christakis Maria,Eniser Hasan Ferit,Hermanns Holger,Hoffmann Jörg,Kothari Yugesh,Li Jianlin,Navas Jorge A.,Wüstholz Valentin
Abstract
AbstractState-of-the-art program-analysis techniques are not yet able to effectively verify safety properties of heterogeneous systems, that is, systems with components implemented using diverse technologies. This shortcoming is pinpointed by programs invoking neural networks despite their acclaimed role as innovation drivers across many application areas. In this paper, we embark on the verification of system-level properties for systems characterized by interaction between programs and neural networks. Our technique provides a tight two-way integration of a program and a neural-network analysis and is formalized in a general framework based on abstract interpretation. We evaluate its effectiveness on 26 variants of a widely used, restricted autonomous-driving benchmark.
Publisher
Springer International Publishing
Reference70 articles.
1. Albarghouthi, A., Li, Y., Gurfinkel, A., Chechik, M.: UFO: A framework for abstraction- and interpolation-based software verification. In: CAV. LNCS, vol. 7358, pp. 672–678. Springer (2012) 2. Alshiekh, M., Bloem, R., Ehlers, R., Könighofer, B., Niekum, S., Topcu, U.: Safe reinforcement learning via shielding. In: AAAI. pp. 2669–2678. AAAI (2018) 3. Babaev, D., Savchenko, M., Tuzhilin, A., Umerenkov, D.: E.T.-RNN: Applying deep learning to credit loan applications. In: KDD. pp. 2183—2190. ACM (2019) 4. Baier, C., Christakis, M., Gros, T.P., Groß, D., Gumhold, S., Hermanns, H., Hoffmann, J., Klauck, M.: Lab conditions for research on explainable automated decisions. In: TAILOR. LNCS, vol. 12641, pp. 83–90. Springer (2020) 5. Barto, A.G., Bradtke, S.J., Singh, S.P.: Learning to act using real-time dynamic programming. Artif. Intell. 72, 81–138 (1995)
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