Efficient Neural Network Robustness Certification with General Activation FunctionsEfficient Neural Network Robustness Certification with General Activation FunctionsZhang, Huan and Weng, Tsui-Wei and Chen, Pin-Yu and Hsieh, Cho-Jui and Daniel, Luca2018
Paper summarydavidstutzZhang 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/).
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/).