Paper:
Published as a conference paper at ICLR 2018 SPATIALLY TRANSFORMED ADVERSARIAL EXAMPLES
Recent studies show that widely used deep neural networks (DNNs) are vulnerable to carefully crafted adversarial examples. Many advanced algorithms have been proposed to generate adversarial examples by leveraging the L p distance for penalizing perturbations. Researchers have explored different defense methods to defend against such adversarial attacks. While the effectiveness of L p distance as a metric of perceptual quality remains an active research area, in this paper we will instead focus on a different type of perturbation, namely spatial transformation, as opposed to manipulating the pixel values directly as in prior works. Perturbations generated through spatial transformation could result in large L p distance measures, but our extensive experiments show that such spatially transformed adversarial examples are perceptually realistic and more difficult to defend against with existing defense systems. This potentially provides a new direction in adversarial example generation and the design of corresponding defenses. We visualize the spatial transformation based perturbation for different examples and show that our technique can produce realistic adversarial examples with smooth image deformation. Finally, we visualize the attention of deep networks with different types of adversarial examples to better understand how these examples are interpreted.

Review:
This paper creates adversarial images by imposing a flow field on an image such that the new spatially transformed image fools the classifier. They minimize a total variation loss in addition to the adversarial loss to create perceptually plausible adversarial images, this is claimed to be better than the normal L2 loss functions.
Experiments were done on MNIST, CIFAR-10, and ImageNet, which is very useful to see that the attack works with high dimensional images. However, some numbers on ImageNet would be helpful as the high resolution of it make it potentially different than the low-resolution MNIST and CIFAR.
It is a bit concerning to see some parts of Fig. 2. Some of Fig. 2 (especially (b)) became so dotted that it no longer seems an adversarial that a human eye cannot detect. And model B in the appendix looks pretty much like a normal model. It might needs some experiments, either human studies, or to test it against an adversarial detector, to ensure that the resulting adversarials are still indeed adversarials to the human eye. Another good thing to run would be to try the 3x3 average pooling restoration mechanism in the following paper:
Xin Li, Fuxin Li. Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics . ICCV 2017.
to see whether this new type of adversarial example can still be restored by a 3x3 average pooling the image (I suspect that this is harder to restore by such a simple method than the previous FGSM or OPT-type, but we need some numbers).
I also don't think FGSM and OPT are this bad in Fig. 4. Are the authors sure that if more regularization are used these 2 methods no longer fool the corresponding classifiers?
I like the experiment showing the attention heat maps for different attacks. This experiment shows that the spatial transforming attack (stAdv) changes the attention of the classifier for each target class, and is robust to adversarially trained Inception v3 unlike other attacks like FGSM and CW. 
I would likely upgrade to a 7 if those concerns are addressed.
After rebuttal: I am happy with the additional experiments and would like to upgrade to an accept.