DeepHunter: a coverage-guided fuzz testing framework for deep neural networks

The past decade has seen the great potential of applying deep neural network (DNN) based software to safety-critical scenarios, such as autonomous driving. Similar to traditional software, DNNs could exhibit incorrect behaviors, caused by hidden defects, leading to severe accidents and losses. In this paper, we propose DeepHunter, a coverage-guided fuzz testing framework for detecting potential defects of general-purpose DNNs. To this end, we first propose a metamorphic mutation strategy to generate new semantically preserved tests, and leverage multiple extensible coverage criteria as feedback to guide the test generation. We further propose a seed selection strategy that combines both diversity-based and…

• NR
  National Research Foundation Singapore

  Award: NRF2018NCR-NCR005-0001 NRF2018NCR-NSOE003-0001