Towards Proving the Adversarial Robustness of Deep Neural Networks

TL;DR

This paper discusses methods for verifying the adversarial robustness of deep neural networks, aiming to ensure small input perturbations do not lead to misclassification, which is crucial for autonomous vehicle safety.

Contribution

It presents recent work and open questions on verifying neural network robustness, addressing scalability and verification challenges.

Findings

Initial verification techniques show promise for small networks.

Open questions remain on scaling verification to large, real-world networks.

Proving robustness can enhance safety assurances for autonomous systems.

Abstract

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.