Paper:
Neural networks show great vulnerability under the threat of adversarial examples. By adding small perturbation to a clean image, neural networks with high classification accuracy can be completely fooled. One intriguing property of the adversarial examples is transferability. This property allows adversarial examples to transfer to networks of unknown structure, which is harmful even to the physical world. The current way of generating adversarial examples is mainly divided into optimization based and gradient based methods. Liu et al. (2017) conjecture that gradient based methods can hardly produce transferable targeted adversarial examples in black-box attack. However, in this paper, we use a simple technique to improve the transferability and success rate of targeted attacks with gradient based methods. We prove that gradient based methods can also generate transferable adversarial examples in targeted attacks. Specifically, we use knowledge distillation for gradient based methods, and show that the transferability can be improved by effectively utilizing different classes of information. Unlike the usual applications of knowledge distillation, we did not train a student network to generate adversarial examples. We take advantage of the fact that knowledge distillation can soften the target and obtain higher information, and combine the soft target and hard target of the same network as the loss function. Our method is generally applicable to most gradient based attack methods.

Review:
The paper suggests to use temperature scaling in adversarial attack design for improving transferability under black-box attack setting. Based on this, the paper proposes several new attacks: D-FGSM, D-MIFGSM, and their ensemble versions. Experimental results found that the proposed methods improves transferability from VGG networks, compared to the non-distillated counterparts. 
In overall, I liked its novel motivation and simplicity of the method, but it seems to me the manuscript should be improved to meet the ICLR standard. Firstly, the presentation of the method is not that clear to me. The mathematical notations are quite confusing for me as most of them are used without any definitions. I am still not convinced that the arguments in Section 3.1 and 3.2 are indeed relevant to the actual practice of black-box adversarial attacks, which usually includes extremely non-smooth boundaries with multiple gradient steps. Even though the experiments show effectiveness partially on VGGNets, but the overall improvements are not sufficient for me to claim the general effectiveness of the method unless the paper could provide additional results on broader range of architectures and  threat models. 
- I feel Section 2.3 is too subjective with vague statements. The following statement was particularly unclear to me: "The first problem with gradient based methods is that they lose their effectiveness after a certain number of iterations.": Does the term "effectiveness" indicate some relative effectiveness compared to other methods, e.g. optimization-based attacks? Is this really a general phenomenon in gradient-based attacks? Also, please elaborate more on "So, insufficient information acquisition for different categories and premature stop of gradient update are the reasons ..."
- Regarding that the softmax is the problem, one could try to directly minimize the logit layers skipping the softmax, i.e., gradient on logits? This is actually one of common techniques and there are many simple tricks in the context of adversarial attack, so the paper may include comparisons with such of tricks as well. 
- It is important to specify the exact threat model used throughout the experiments, e.g. perturbation constraints and attack details. Demonstrating the effectiveness on a variety of threat models could also strengthen the manuscript.
- Table 1 and 2 may include other baseline (black-box attack) methods for comparison. This would much help to understand the method better.