Poster: Effective layers in coverage metrics for deep neural networks

Leo Hyun Park, Sangjin Oh, Jaeuk Kim, Soochang Chung, Taekyoung Kwon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deep neural networks (DNNs) gained in popularity as an effective machine learning algorithm, but their high complexity leads to the lack of model interpretability and difficulty in the verification of deep learning. Fuzzing, which is an automated software testing technique, is recently applied to DNNs as an effort to address these problems by following the trend of coverage-based fuzzing. However, new coverage metrics on DNNs may bring out the question of which layer to measure the coverage in DNNs. In this poster, we empirically evaluate the performance of existing coverage metrics. By the comparative analysis of experimental results, we compile the most effective layer for each of coverage metrics and discuss a future direction of DNN fuzzing.

Original languageEnglish
Title of host publicationCCS 2019 - Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery
Pages2681-2683
Number of pages3
ISBN (Electronic)9781450367479
DOIs
Publication statusPublished - 2019 Nov 6
Event26th ACM SIGSAC Conference on Computer and Communications Security, CCS 2019 - London, United Kingdom
Duration: 2019 Nov 112019 Nov 15

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221

Conference

Conference26th ACM SIGSAC Conference on Computer and Communications Security, CCS 2019
CountryUnited Kingdom
CityLondon
Period19/11/1119/11/15

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

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  • Cite this

    Park, L. H., Oh, S., Kim, J., Chung, S., & Kwon, T. (2019). Poster: Effective layers in coverage metrics for deep neural networks. In CCS 2019 - Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (pp. 2681-2683). (Proceedings of the ACM Conference on Computer and Communications Security). Association for Computing Machinery. https://doi.org/10.1145/3319535.3363286