Coverage-guided fuzzing for feedforward neural networks

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

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

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1162-1165
Number of pages4
ISBN (Electronic)9781728125084
DOIs
Publication statusPublished - Nov 2019
Event34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019 - San Diego, United States
Duration: Nov 10 2019Nov 15 2019

Publication series

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

Conference

Conference34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
CountryUnited States
CitySan Diego
Period11/10/1911/15/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software
  • Control and Optimization

Fingerprint Dive into the research topics of 'Coverage-guided fuzzing for feedforward neural networks'. Together they form a unique fingerprint.

Cite this