@inproceedings{706d23f102ed443e9ed22691faa9083a,
title = "Demo: The Neural Network Verification (NNV) Tool",
abstract = "NNV (Neural Network Verification) is a framework for the verification of deep neural networks (DNNs) and learning-enabled cyber-physical systems (CPS) inspired by a collection of reachability algorithms that make use of a variety of set representations such as the star set. NNV supports exact and over-approximate reachability algorithms used to verify the safety and robustness of feed-forward neural networks (FFNNs). These two analysis schemes are also used for learning enabled CPS, i.e., closed-loop systems, and particularly in neural network control systems with linear models and FFNN controllers with piecewise-linear activation functions. Additionally, NNV supports over-approximate analysis for nonlinear plant models by combining the star set analysis used for FFNNs with the zonotope-based analysis for nonlinear plant dynamics provided by CORA. This demo paper demonstrates NNV's capabilities by considering a case study of the verification of a learning-enabled adaptive cruise control system. ",
author = "Tran, {Hoang Dung} and Lopez, {Diego Manzanas} and Xiaodong Yang and Patrick Musau and Nguyen, {Luan Viet} and Weiming Xiang and Stanley Bak and Johnson, {Taylor T.}",
note = "Funding Information: ACKNOWLEDGMENT The material presented in this paper is based upon work supported by the National Science Foundation (NSF) under grant numbers SHF 1910017 and FMitF 1918450, the Air Force Office of Scientific Research (AFOSR) through contract number FA9550-18-1-0122, and the Defense Advanced Research Projects Agency (DARPA) through contract number FA8750-18-C-0089. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of AFOSR, DARPA, or NSF. Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Workshop on Design Automation for CPS and IoT, DESTION 2020 ; Conference date: 21-04-2020",
year = "2020",
month = apr,
doi = "10.1109/DESTION50928.2020.00010",
language = "English (US)",
series = "Proceedings - 2020 IEEE Workshop on Design Automation for CPS and IoT, DESTION 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "21--22",
booktitle = "Proceedings - 2020 IEEE Workshop on Design Automation for CPS and IoT, DESTION 2020",
}