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ASE 2019

Coverage-Guided Fuzzing for Feedforward Neural Networks

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

Full textDOI 10.1109/ase.2019.00127dblp

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.

BibTeX
@inproceedings{Xie-al:ASE19,
  author    = {Xiaofei Xie and
               Hongxu Chen and
               Yi Li and
               Lei Ma and
               Yang Liu and
               Jianjun Zhao},
  title     = {{Coverage-Guided} Fuzzing for Feedforward Neural Networks},
  booktitle = {ASE},
  pages     = {1162--1165},
  publisher = {{IEEE}},
  year      = {2019},
}

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