Reluplex: An efficient smt solver for verifying deep neural networks

Guy Katz*, Clark Barrett, David L. Dill, Kyle Julian, Mykel J. Kochenderfer

*Corresponding author for this work

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

901 Scopus citations

Abstract

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.

Original languageAmerican English
Title of host publicationComputer Aided Verification - 29th International Conference, CAV 2017, Proceedings
EditorsViktor Kuncak, Rupak Majumdar
PublisherSpringer Verlag
Pages97-117
Number of pages21
ISBN (Print)9783319633862
DOIs
StatePublished - 2017
Externally publishedYes
Event29th International Conference on Computer Aided Verification, CAV 2017 - Heidelberg, Germany
Duration: 24 Jul 201728 Jul 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10426 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference29th International Conference on Computer Aided Verification, CAV 2017
Country/TerritoryGermany
CityHeidelberg
Period24/07/1728/07/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

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