Toward Scalable Verification for Safety-Critical Deep Networks

By L. Kuper, Guy Katz, Justin Emile Gottschlich, Kyle D. Julian, Clark W. Barrett, Mykel J. Kochenderfer. 2018

40 citations recorded by the source corpus.

The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Formal verification can address these concerns by guaranteeing that a deep learning system operates as intended, but the state-of-the-art is limited to small systems. In this work-in-progress report we give an overview of our work on mitigating this difficulty, by pursuing two complementary directions: devising scalable verification techniques, and identifying design choices that result in deep learning systems that are more amenable to verification.

Subfields: Artificial Intelligence, Software

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Citations in the corpus

The Marabou Framework for Verification and Analysis of Deep Neural Networks (2019, methodology): “The Marabou project builds upon our previous work on the Reluplex project [2,7,12,13,15,17], which focused on applying SMT-based techniques to the verification of DNNs.”