OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks

Xingwu Guo, Ziwei Zhou, Yueling Zhang, Guy Katz, Min Zhang*

*Corresponding author for this work

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

5 Scopus citations

Abstract

Occlusion is a prevalent and easily realizable semantic perturbation to deep neural networks (DNNs). It can fool a DNN into misclassifying an input image by occluding some segments, possibly resulting in severe errors. Therefore, DNNs planted in safety-critical systems should be verified to be robust against occlusions prior to deployment. However, most existing robustness verification approaches for DNNs are focused on non-semantic perturbations and are not suited to the occlusion case. In this paper, we propose the first efficient, SMT-based approach for formally verifying the occlusion robustness of DNNs. We formulate the occlusion robustness verification problem and prove it is NP-complete. Then, we devise a novel approach for encoding occlusions as a part of neural networks and introduce two acceleration techniques so that the extended neural networks can be efficiently verified using off-the-shelf, SMT-based neural network verification tools. We implement our approach in a prototype called OccRob and extensively evaluate its performance on benchmark datasets with various occlusion variants. The experimental results demonstrate our approach’s effectiveness and efficiency in verifying DNNs’ robustness against various occlusions, and its ability to generate counterexamples when these DNNs are not robust.

Original languageAmerican English
Title of host publicationTools and Algorithms for the Construction and Analysis of Systems - 29th International Conference, TACAS 2023, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022, Proceedings
EditorsSriram Sankaranarayanan, Natasha Sharygina
PublisherSpringer Science and Business Media Deutschland GmbH
Pages208-226
Number of pages19
ISBN (Print)9783031308222
DOIs
StatePublished - 2023
Event29th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2023, held as part of the 26th European Joint Conferences on Theory and Practice of Software, ETAPS 2023 - Paris, France
Duration: 22 Apr 202327 Apr 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13993 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference29th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2023, held as part of the 26th European Joint Conferences on Theory and Practice of Software, ETAPS 2023
Country/TerritoryFrance
CityParis
Period22/04/2327/04/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

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