Aster : Encoding Data Augmentation Relations into Seed Test Suites for Robustness Assessment and Fuzzing of Data-Augmented Deep Learning Models

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security
Subtitle of host publicationQRS 2023
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages370-381
ISBN (electronic)9798350319583
ISBN (print)979-8-3503-1959-0
Publication statusPublished - 2023

Publication series

NameIEEE International Conference on Software Quality, Reliability and Security, QRS
ISSN (Print)2693-9177

Conference

Title23rd IEEE International Conference on Software Quality, Reliability, and Security (QRS 2023)
LocationChiang Mai Marriott Hotel
PlaceThailand
CityChiang Mai
Period22 - 26 October 2023

Abstract

Data-augmented deep learning models are widely used in real-world applications. However, many state-of the-art loss-based or coverage-based fuzzing techniques fail to produce fuzzing samples for them from many seeds. This paper proposes Aster, a novel technique to address this problem to enhance their fuzzing effectiveness for deep learning models trained with multi-sample data augmentation methods. Aster formulates a novel reachability-based strategy to encode the insights of every seed's direct and indirect data augmentation relation instances into the replacement seed of that seed systematically. Our experiment shows that Aster is highly effective. On average, loss-based and coverage-based fuzzing techniques can generate 166% and 110% more fuzzing samples and reduce 31% and 22% unsuccessful seeds, respectively, after adopting the replacement seeds generated by Aster to replace their original seeds. Their improved models also become up to 55% and 40% on average more robust against FGSM and PGD attacks in the experiment. © 2023 IEEE

Research Area(s)

  • data augmentation, fuzzing, neural network, robustness, seed generation, testing

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Aster: Encoding Data Augmentation Relations into Seed Test Suites for Robustness Assessment and Fuzzing of Data-Augmented Deep Learning Models. / Wang, Haipeng; Wei, Zhengyuan; Zhou, Qilin et al.
Proceedings - 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security: QRS 2023. Institute of Electrical and Electronics Engineers, Inc., 2023. p. 370-381 (IEEE International Conference on Software Quality, Reliability and Security, QRS).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review