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Fast and Effective Robustness Certification
Gagandeep Singh · Timon Gehr · Matthew Mirman · Markus Püschel · Martin Vechev

Tue Dec 04 07:45 AM -- 09:45 AM (PST) @ Room 210 #26
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 and convolutional 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 $L_{\infty}$ attack with $\epsilon = 0.1$ with an average runtime of 133 seconds.

Author Information

Gagandeep Singh (ETH Zurich)
Timon Gehr (ETH Zurich)
Matthew Mirman (ETH Zurich)
Markus Püschel (ETH Zurich)
Martin Vechev (DeepCode and ETH Zurich, Switzerland)

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