Article

SyReNN: A Tool for Analyzing Deep Neural Networks

Authors:

Matthew Sotoudeh,

Aditya V. ThakurAuthors Info & Claims

Tools 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, Luxembourg City, Luxembourg, March 27 – April 1, 2021, Proceedings, Part II

Pages 281 - 302

https://doi.org/10.1007/978-3-030-72013-1_15

Published: 27 March 2021 Publication History

Abstract

Deep Neural Networks (DNNs) are rapidly gaining popularity in a variety of important domains. Formally, DNNs are complicated vector-valued functions which come in a variety of sizes and applications. Unfortunately, modern DNNs have been shown to be vulnerable to a variety of attacks and buggy behavior. This has motivated recent work in formally analyzing the properties of such DNNs. This paper introduces SyReNN, a tool for understanding and analyzing a DNN by computing its symbolic representation. The key insight is to decompose the DNN into linear functions. Our tool is designed for analyses using low-dimensional subsets of the input space, a unique design point in the space of DNN analysis tools. We describe the tool and the underlying theory, then evaluate its use and performance on three case studies: computing Integrated Gradients, visualizing a DNN’s decision boundaries, and patching a DNN.

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cover image Guide Proceedings

Tools 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, Luxembourg City, Luxembourg, March 27 – April 1, 2021, Proceedings, Part II

Mar 2021

475 pages

ISBN:978-3-030-72012-4

DOI:10.1007/978-3-030-72013-1

Editors:
Jan Friso Groote
Eindhoven University of Technology, Eindhoven, The Netherlands
,
Kim Guldstrand Larsen
Aalborg University, Aalborg East, Denmark

© The Author(s) 2021.

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Cited By

Tao ZNawas SMitchell JThakur A(2023)Architecture-Preserving Provable Repair of Deep Neural NetworksProceedings of the ACM on Programming Languages10.1145/35912387:PLDI(443-467)Online publication date: 6-Jun-2023
https://dl.acm.org/doi/10.1145/3591238
Scott JPan GJha PKhalil EGanesh V(2023)Pierce: A Testing Tool for Neural Network Verification SolversVerified Software. Theories, Tools and Experiments10.1007/978-3-031-66064-1_3(31-43)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-66064-1_3
Mukhopadhyay DMadhukar KSrivas M(2022)Permutation Invariance of Deep Neural Networks with ReLUsNASA Formal Methods10.1007/978-3-031-06773-0_17(318-337)Online publication date: 24-May-2022
https://dl.acm.org/doi/10.1007/978-3-031-06773-0_17
Sotoudeh MThakur AFreund SYahav E(2021)Provable repair of deep neural networksProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation10.1145/3453483.3454064(588-603)Online publication date: 19-Jun-2021
https://dl.acm.org/doi/10.1145/3453483.3454064

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