Verifying Properties of Binarized Deep Neural Networks

Authors

  • Nina Narodytska VMware Research
  • Shiva Kasiviswanathan Amazon
  • Leonid Ryzhyk VMware Research
  • Mooly Sagiv VMware Research
  • Toby Walsh UNSW; Data61

DOI:

https://doi.org/10.1609/aaai.v32i1.12206

Keywords:

binarized deep neural networks, verification, SAT, counterexample-guided search

Abstract

Understanding properties of deep neural networks is an important challenge in deep learning. In this paper, we take a step in this direction by proposing a rigorous way of verifying properties of a popular class of neural networks, Binarized Neural Networks, using the well-developed means of Boolean satisfiability. Our main contribution is a construction that creates a representation of a binarized neural network as a Boolean formula. Our encoding is the first exact Boolean representation of a deep neural network. Using this encoding, we leverage the power of modern SAT solvers along with a proposed counterexample-guided search procedure to verify various properties of these networks. A particular focus will be on the critical property of robustness to adversarial perturbations. For this property, our experimental results demonstrate that our approach scales to medium-size deep neural networks used in image classification tasks. To the best of our knowledge, this is the first work on verifying properties of deep neural networks using an exact Boolean encoding of the network.

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Published

2018-04-26

How to Cite

Narodytska, N., Kasiviswanathan, S., Ryzhyk, L., Sagiv, M., & Walsh, T. (2018). Verifying Properties of Binarized Deep Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.12206

Issue

Section

Main Track: Search and Constraint Satisfaction