Paper:
Published as a conference paper at ICLR 2018 STOCHASTIC ACTIVATION PRUNING FOR ROBUST ADVERSARIAL DEFENSE
Neural networks are known to be vulnerable to adversarial examples. Carefully chosen perturbations to real images, while imperceptible to humans, induce misclassification and threaten the reliability of deep learning systems in the wild. To guard against adversarial examples, we take inspiration from game theory and cast the problem as a minimax zero-sum game between the adversary and the model. In general, for such games, the optimal strategy for both players requires a stochastic policy, also known as a mixed strategy. In this light, we propose Stochastic Activation Pruning (SAP), a mixed strategy for adversarial defense. SAP prunes a random subset of activations (preferentially pruning those with smaller magnitude) and scales up the survivors to compensate. We can apply SAP to pretrained networks, including adversarially trained models, without fine-tuning, providing robustness against adversarial examples. Experiments demonstrate that SAP confers robustness against attacks, increasing accuracy and preserving calibration.

Review:
This paper investigates a new approach to prevent a given classifier from adversarial examples. The most important contribution is that the proposed algorithm can be applied post-hoc to already trained networks. Hence, the proposed algorithm (Stochastic Activation Pruning) can be combined with algorithms which prevent from adversarial examples during the training.
The proposed algorithm is clearly described. However there are issues in the presentation.
In section 2-3, the problem setting is not suitably introduced.
In particular one sentence that can be misleading:
“Given a classifier, one common way to generate an adversarial example is to perturb the input in direction of the gradient…”
You should explain that given a classifier with stochastic output, the optimal way to generate an adversarial example is to perturb the input proportionally to the gradient. The practical way in which the adversarial examples are generated is not known to the player. An adversary could choose any policy. The only thing the player knows is the best adversarial policy.
In section 4, I do not understand why the adversary uses only the sign and not also the value of the estimated gradient. Does it come from a high variance? If it is the case, you should explain that the optimal policy of the adversary is approximated by “fast gradient sign method”. 
In comparison to dropout algorithm, SAP shows improvements of accuracy against adversarial examples. SAP does not perform as well as adversarial training, but SAP could be used with a trained network. 
Overall, this paper presents a practical method to prevent a classifier from adversarial examples, which can be applied in addition to adversarial training. The presentation could be improved.