Towards Proving the Adversarial Robustness of Deep Neural Networks

Guy Katz
(Stanford University)
Clark Barrett
(Stanford University)
David L. Dill
(Stanford University)
Kyle Julian
(Stanford University)
Mykel J. Kochenderfer
(Stanford University)

Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations. Manually crafting software controllers for these vehicles is difficult, but there has been some success in using deep neural networks generated using machine-learning. However, deep neural networks are opaque to human engineers, rendering their correctness very difficult to prove manually; and existing automated techniques, which were not designed to operate on neural networks, fail to scale to large systems. This paper focuses on proving the adversarial robustness of deep neural networks, i.e. proving that small perturbations to a correctly-classified input to the network cannot cause it to be misclassified. We describe some of our recent and ongoing work on verifying the adversarial robustness of networks, and discuss some of the open questions we have encountered and how they might be addressed.

In Lukas Bulwahn, Maryam Kamali and Sven Linker: Proceedings First Workshop on Formal Verification of Autonomous Vehicles (FVAV 2017), Turin, Italy, 19th September 2017, Electronic Proceedings in Theoretical Computer Science 257, pp. 19–26.
Published: 7th September 2017.

ArXived at: http://dx.doi.org/10.4204/EPTCS.257.3 bibtex PDF
References in reconstructed bibtex, XML and HTML format (approximated).
Comments and questions to: eptcs@eptcs.org
For website issues: webmaster@eptcs.org