# DAVIDSTUTZ

23thOCTOBER2019

### READING

Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, Luca Daniel. Efficient Neural Network Robustness Certification with General Activation Functions. NeurIPS 2018: 4944-4953.

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 on ShortScience.org.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below: