An SMT-Based Approach for Verifying Binarized Neural Networks

Guy Amir, Haoze Wu, Clark Barrett, Guy Katz*

*Corresponding author for this work

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

24 Scopus citations

Abstract

Deep learning has emerged as an effective approach for creating modern software systems, with neural networks often surpassing hand-crafted systems. Unfortunately, neural networks are known to suffer from various safety and security issues. Formal verification is a promising avenue for tackling this difficulty, by formally certifying that networks are correct. We propose an SMT-based technique for verifying binarized neural networks — a popular kind of neural network, where some weights have been binarized in order to render the neural network more memory and energy efficient, and quicker to evaluate. One novelty of our technique is that it allows the verification of neural networks that include both binarized and non-binarized components. Neural network verification is computationally very difficult, and so we propose here various optimizations, integrated into our SMT procedure as deduction steps, as well as an approach for parallelizing verification queries. We implement our technique as an extension to the Marabou framework, and use it to evaluate the approach on popular binarized neural network architectures.

Original languageAmerican English
Title of host publicationTools and Algorithms for the Construction and Analysis of Systems - 27th International Conference, TACAS 2021 Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021
EditorsJan Friso Groote, Kim Guldstrand Larsen
PublisherSpringer Science and Business Media Deutschland GmbH
Pages203-222
Number of pages20
ISBN (Print)9783030720124
DOIs
StatePublished - 2021
Event27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021 Held as Part of 24th European Joint Conferences on Theory and Practice of Software, ETAPS 2021 - Virtual, Online
Duration: 27 Mar 20211 Apr 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12652 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2021 Held as Part of 24th European Joint Conferences on Theory and Practice of Software, ETAPS 2021
CityVirtual, Online
Period27/03/211/04/21

Bibliographical note

Publisher Copyright:
© The Author(s) 2021.

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