The Marabou Framework for Verification and Analysis of Deep Neural Networks

Guy Katz*, Derek A. Huang, Duligur Ibeling, Kyle Julian, Christopher Lazarus, Rachel Lim, Parth Shah, Shantanu Thakoor, Haoze Wu, Aleksandar Zeljić, David L. Dill, Mykel J. Kochenderfer, Clark Barrett

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

256 Scopus citations

Abstract

Deep neural networks are revolutionizing the way complex systems are designed. Consequently, there is a pressing need for tools and techniques for network analysis and certification. To help in addressing that need, we present Marabou, a framework for verifying deep neural networks. Marabou is an SMT-based tool that can answer queries about a network’s properties by transforming these queries into constraint satisfaction problems. It can accommodate networks with different activation functions and topologies, and it performs high-level reasoning on the network that can curtail the search space and improve performance. It also supports parallel execution to further enhance scalability. Marabou accepts multiple input formats, including protocol buffer files generated by the popular TensorFlow framework for neural networks. We describe the system architecture and main components, evaluate the technique and discuss ongoing work.

Original languageAmerican English
Title of host publicationComputer Aided Verification - 31st International Conference, CAV 2019, Proceedings
EditorsIsil Dillig, Serdar Tasiran
PublisherSpringer Verlag
Pages443-452
Number of pages10
ISBN (Print)9783030255398
DOIs
StatePublished - 2019
Event31st International Conference on Computer Aided Verification, CAV 2019 - New York City, United States
Duration: 15 Jul 201918 Jul 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11561 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference31st International Conference on Computer Aided Verification, CAV 2019
Country/TerritoryUnited States
CityNew York City
Period15/07/1918/07/19

Bibliographical note

Publisher Copyright:
© The Author(s). 2019.

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