Coverage-guided fuzzing for feedforward neural networks

Xiaofei Xie, Hongxu Chen, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao

研究成果: 著書/レポートタイプへの貢献会議での発言

抜粋

Deep neural network (DNN) has been widely applied to safety-critical scenarios such as autonomous vehicle, security surveillance, and cyber-physical control systems. Yet, the incorrect behaviors of DNNs can lead to severe accidents and tremendous losses due to hidden defects. In this paper, we present DeepHunter, a general-purpose fuzzing framework for detecting defects of DNNs. DeepHunter is inspired by traditional grey-box fuzzing and aims to increase the overall test coverage by applying adaptive heuristics according to runtime feedback. Specifically, DeepHunter provides a series of seed selection strategies, metamorphic mutation strategies, and testing criteria customized to DNN testing; all these components support multiple built-in configurations which are easy to extend. We evaluated DeepHunter on two popular datasets and the results demonstrate the effectiveness of DeepHunter in achieving coverage increase and detecting real defects. A video demonstration which showcases the main features of DeepHunter can be found at https://youtu.be/s5DfLErcgrc.

元の言語英語
ホスト出版物のタイトルProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
出版者Institute of Electrical and Electronics Engineers Inc.
ページ1162-1165
ページ数4
ISBN(電子版)9781728125084
DOI
出版物ステータス出版済み - 11 2019
イベント34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019 - San Diego, 米国
継続期間: 11 10 201911 15 2019

出版物シリーズ

名前Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019

会議

会議34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
米国
San Diego
期間11/10/1911/15/19

    フィンガープリント

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software
  • Control and Optimization

これを引用

Xie, X., Chen, H., Li, Y., Ma, L., Liu, Y., & Zhao, J. (2019). Coverage-guided fuzzing for feedforward neural networks. : Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019 (pp. 1162-1165). [8952279] (Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASE.2019.00127