Fast and effective robustness certification

Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus Püschel, Martin Vechev

Research output: Contribution to journalConference articlepeer-review


We present a new method and system, called DeepZ, for certifying neural network robustness based on abstract interpretation. Compared to state-of-the-art automated verifiers for neural networks, DeepZ: (i) handles ReLU, Tanh and Sigmoid activation functions, (ii) supports feedforward, convolutional, and residual architectures, (iii) is significantly more scalable and precise, and (iv) and is sound with respect to floating point arithmetic. These benefits are due to carefully designed approximations tailored to the setting of neural networks. As an example, DeepZ achieves a verification accuracy of 97% on a large network with 88, 500 hidden units under L attack with = 0.1 with an average runtime of 133 seconds.

Original languageEnglish (US)
Pages (from-to)10802-10813
Number of pages12
JournalAdvances in Neural Information Processing Systems
StatePublished - 2018
Externally publishedYes
Event32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada
Duration: Dec 2 2018Dec 8 2018

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


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