Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training

Jiaxu Tian, Dapeng Zhi, Si Liu, Peixin Wang, Guy Katz, Min Zhang*

*Corresponding author for this work

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

Abstract

The intrinsic complexity of deep neural networks (DNNs) makes it challenging to verify not only the networks themselves but also the hosting DNN-controlled systems. Reachability analysis of these systems faces the same challenge. Existing approaches rely on over-approximating DNNs using simpler polynomial models. However, they suffer from low efficiency and large overestimation, and are restricted to specific types of DNNs. This paper presents a novel abstraction-based approach to bypass the crux of over-approximating DNNs in reachability analysis. Specifically, we extend conventional DNNs by inserting an additional abstraction layer, which abstracts a real number to an interval for training. The inserted abstraction layer ensures that the values represented by an interval are indistinguishable to the network for both training and decision-making. Leveraging this, we devise the first black-box reachability analysis approach for DNN-controlled systems, where trained DNNs are only queried as black-box oracles for the actions on abstract states. Our approach is sound, tight, efficient, and agnostic to any DNN type and size. The experimental results on a wide range of benchmarks show that the DNNs trained by using our approach exhibit comparable performance, while the reachability analysis of the corresponding systems becomes more amenable with significant tightness and efficiency improvement over the state-of-the-art white-box approaches.

Original languageAmerican English
Title of host publicationVerification, Model Checking, and Abstract Interpretation - 25th International Conference, VMCAI 2024, Proceedings
EditorsRayna Dimitrova, Ori Lahav, Sebastian Wolff
PublisherSpringer Science and Business Media Deutschland GmbH
Pages73-97
Number of pages25
ISBN (Print)9783031505201
DOIs
StatePublished - 2024
Event25th International Conference on Verification, Model Checking, and Abstract Interpretation, VMCAI 2024 was co-located with 51st ACM SIGPLAN Symposium on Principles of Programming Languages, POPL 2024 - London, United Kingdom
Duration: 15 Jan 202416 Jan 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14500 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Conference on Verification, Model Checking, and Abstract Interpretation, VMCAI 2024 was co-located with 51st ACM SIGPLAN Symposium on Principles of Programming Languages, POPL 2024
Country/TerritoryUnited Kingdom
CityLondon
Period15/01/2416/01/24

Bibliographical note

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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