Paper:
Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial examples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate adversarial perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply AdvGAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge (Mądry et al., 2017b).

Review:
I thank the authors for the thoughtful response and rebuttal. The authors have substantially updated their manuscript and improved the presentation.
Re: Speed. I brought up this point because this was a bulleted item in the Introduction in the earlier version of the manuscript. In the revised manuscript, this bullet point is now removed. I will take this point to be moot.
Re: High resolution. The authors point to recent GAN literature that provides some first results with high resolution GANs but I do not see quantitative evidence in the high resolution setting for this paper. (Figure 4 provides qualitative examples from ImageNet but no quantitative assessment.)
Because the authors improved the manuscript, I upwardly revised my score to 'Ok but not good enough - rejection'. I am not able to accept this paper because of the latter point.
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The authors present an interesting new method for generating adversarial examples. Namely, the author train a generative adversarial network (GAN) to adversarial examples for a target network. The authors demonstrate that the network works well in the semi-white box and black box settings.
The authors wrote a clear paper with great references and clear descriptions.
My primary concern is that this work has limited practical benefit in a realistic setting. Addressing each and every concern is quite important:
1) Speed. The authors suggest that training a GAN provides a speed benefit with respect to other attack techniques. The FGSM method (Goodfellow et al, 2015) is basically 1 inference operation and 1 backward operation. The GAN is 1 forward operation. Granted this results in a small difference in timing 0.06s versus 0.01s, however it would seem that avoiding a backward pass is a somewhat small speed gain.
 
Furthermore, I would want to question the practical usage of having an 'even faster' method for generating adversarial examples. What is the reason that we need to run adversarial attacks 'even faster'? I am not aware of any use-cases, but if there are some, the authors should describe the rationales at length in their paper.
2) High spatial resolution images. Previous methods, e.g. FGSM, may work on arbitrarily sized images. At best, GANs generate reasonable images that are lower resolutions (e.g. < 128x128). Building GAN's that operate above-and-beyond moderate spatial resolution is an open research topic. The best GAN models for generating high resolution images are  difficult to train and it is not clear if they would work in this setting. Furthermore, images with even higher resolutions, e.g. 512x512, which is quite common in ImageNet, are difficult to synthesizes using current techniques.
3) Controlling the amount of distortion. A feature of previous optimization based methods is that a user may specify the amount of perturbation (epsilon). This is a key feature if not requirement in an adversarial perturbation because a user might want to examine the performance of a given model as a function of epsilon. Performing such an analysis with this model is challenging (i.e. retraining a GAN) and it is not clear if a given image generated by a GAN will always achieve a given epsilon perturbation/
On a more minor note, the authors suggest that generating a *diversity* of adversarial images is of practical import. I do not see the utility of being able to generate a diversity of adversarial images. The authors need to provide more justification for this motivation.