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Continual Assurance of Learning-Enabled, Cyber-Physical Systems (LE-CPS)

NNV: A Tool for Verification of Deep Neural Networks and Learning-Enabled Autonomous Cyber-Physical Systems

Abstract

This paper presents the Neural Network Verification (NNV) software tool, a set-based verification framework for deep neural networks (DNNs) and learning-enabled cyber-physical systems (CPS). The crux of NNV is a collection of reachability algorithms that make use of a variety of set representations, such as polyhedra, star sets, zonotopes, and abstract-domain representations. NNV supports both exact (sound and complete) and over-approximate (sound) reachability algorithms for verifying safety and robustness properties of feed-forward neural networks (FFNNs) with various activation functions. For learning-enabled CPS, such as closed-loop control systems incorporating neural networks, NNV provides exact and over-approximate reachability analysis schemes for linear plant models and FFNN controllers with piecewise-linear activation functions, such as ReLUs. For similar neural network control systems (NNCS) that instead have nonlinear plant models, NNV supports over-approximate analysis by combining the star set analysis used for FFNN controllers with zonotope-based analysis for nonlinear plant dynamics building on CORA. We evaluate NNV using two real-world case studies: the first is safety verification of ACAS Xu networks, and the second deals with the safety verification of a deep learning-based adaptive cruise control system.

Year of Publication
2020
Conference Name
32nd International Conference on Computer-Aided Verification 2020
Publisher
Springer, Cham
Conference Location
Los Angeles, California, USA
ISBN Number
978-3-030-53288-8
DOI
10.1007/978-3-030-53288-8_1