Efficient Neural Network Robustness Certification with General Activation Functions Efficient Neural Network Robustness Certification with General Activation Functions
Paper summary Zhang et al. propose CROWN, a method for certifying adversarial robustness based on bounding activations functions using linear functions. Informally, the main result can be stated as follows: if the activation functions used in a deep neural network can be bounded above and below by linear functions (the activation function may also be segmented first), the network output can also be bounded by linear functions. These linear functions can be computed explicitly, as stated in the paper. Then, given an input example $x$ and a set of allowed perturbations, usually constrained to a $L_p$ norm, these bounds can be used to obtain a lower bound on the robustness of networks. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
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Efficient Neural Network Robustness Certification with General Activation Functions
Zhang, Huan and Weng, Tsui-Wei and Chen, Pin-Yu and Hsieh, Cho-Jui and Daniel, Luca
Neural Information Processing Systems Conference - 2018 via Local Bibsonomy
Keywords: dblp


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Summary by David Stutz 1 month ago
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