Boosting robustness certification of neural networks

Gagandeep Singh; Timon Gehr; Markus Püschel; Martin Vechev

Boosting robustness certification of neural networks

Gagandeep Singh, Timon Gehr, Markus Püschel, Martin Vechev

Research output: Contribution to conference › Paper › peer-review

Abstract

We present a novel approach for the certification of neural networks against adversarial perturbations which combines scalable overapproximation methods with precise (mixed integer) linear programming. This results in significantly better precision than state-of-the-art verifiers on challenging feedforward and convolutional neural networks with piecewise linear activation functions.

Original language	English (US)
State	Published - 2019
Externally published	Yes
Event	7th International Conference on Learning Representations, ICLR 2019 - New Orleans, United States Duration: May 6 2019 → May 9 2019

Conference

Conference	7th International Conference on Learning Representations, ICLR 2019
Country/Territory	United States
City	New Orleans
Period	5/6/19 → 5/9/19

ASJC Scopus subject areas

Education
Computer Science Applications
Linguistics and Language
Language and Linguistics

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title = "Boosting robustness certification of neural networks",

abstract = "We present a novel approach for the certification of neural networks against adversarial perturbations which combines scalable overapproximation methods with precise (mixed integer) linear programming. This results in significantly better precision than state-of-the-art verifiers on challenging feedforward and convolutional neural networks with piecewise linear activation functions.",

author = "Gagandeep Singh and Timon Gehr and Markus P{\"u}schel and Martin Vechev",

note = "Publisher Copyright: {\textcopyright} 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.; 7th International Conference on Learning Representations, ICLR 2019 ; Conference date: 06-05-2019 Through 09-05-2019",

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AU - Singh, Gagandeep

AU - Gehr, Timon

AU - Püschel, Markus

AU - Vechev, Martin

PY - 2019

Y1 - 2019

N2 - We present a novel approach for the certification of neural networks against adversarial perturbations which combines scalable overapproximation methods with precise (mixed integer) linear programming. This results in significantly better precision than state-of-the-art verifiers on challenging feedforward and convolutional neural networks with piecewise linear activation functions.

AB - We present a novel approach for the certification of neural networks against adversarial perturbations which combines scalable overapproximation methods with precise (mixed integer) linear programming. This results in significantly better precision than state-of-the-art verifiers on challenging feedforward and convolutional neural networks with piecewise linear activation functions.

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UR - http://www.scopus.com/inward/citedby.url?scp=85083952572&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85083952572

T2 - 7th International Conference on Learning Representations, ICLR 2019

Y2 - 6 May 2019 through 9 May 2019

ER -

Boosting robustness certification of neural networks

Abstract

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ASJC Scopus subject areas

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