Symbolic Execution for Deep Neural Networks

@article{Gopinath2018SymbolicEF,
  title={Symbolic Execution for Deep Neural Networks},
  author={Divya Gopinath and Kaiyuan Wang and Mengshi Zhang and Corina S. Pasareanu and Sarfraz Khurshid},
  journal={CoRR},
  year={2018},
  volume={abs/1807.10439}
}
Deep Neural Networks (DNN) are increasingly used in a variety of applications, many of them with substantial safety and security concerns. This paper introduces DeepCheck, a new approach for validating DNNs based on core ideas from program analysis, specifically from symbolic execution. The idea is to translate a DNN into an imperative program, thereby enabling program analysis to assist with DNN validation. A basic translation however creates programs that are very complex to analyze… CONTINUE READING
Recent Discussions
This paper has been referenced on Twitter 14 times over the past 90 days. VIEW TWEETS

References

Publications referenced by this paper.
Showing 1-10 of 31 references

Adversarial machine learning at scale

  • R. Feinman, R. R. Curtin, S. Shintre, A. B. Gardner
  • 2016, technical Report. http://arxiv.org/abs…
  • 2016
1 Excerpt

Similar Papers

Loading similar papers…