Intriguing Properties of Adversarial Examples
It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples. In attempting to explain the origin of adversarial examples, previous studies have typically focused on the fact that neural networks operate on high dimensional data, they overfit, or they are too linear. Here we show that distributions of logit differences have a universal functional form. This functional form is independent of architecture, dataset, and training protocol; nor does it change during training. This leads to adversarial error having a universal scaling, as a power-law, with respect to the size of the adversarial perturbation. We show that this universality holds for a broad range of datasets (MNIST, CIFAR10, ImageNet, and random data), models (including state-of-the-art deep networks, linear models, adversarially trained networks, and networks trained on randomly shuffled labels), and attacks (FGSM, step l.l., PGD). Motivated by these results, we study the effects of reducing prediction entropy on adversarial robustness. Finally, we study the effect of network architectures on adversarial sensitivity. To do this, we use neural architecture search with reinforcement learning to find adversarially robust architectures on CIFAR10. Our resulting architecture is more robust to white emph{and} black box attacks compared to previous attempts.
 Very intriguing paper and results to say the least. I like the way it is written, and the neat interpretations that the authors give of what is going on (instead of assuming that readers will see the same). There is a well presented story of experiments to follow which gives us insight into the problem. Interesting insight into defensive distillation and the effects of uncertainty in neural networks. Quality/Clarity: well written and was easy for me to read Originality: Brings both new ideas and unexpected experimental results. Significance: Creates more questions than it answers, which imo is a positive as this topic definitely deserves more research. Remarks: - Maybe re-render Figure 3 at a higher resolution? - The caption of Figure 5 doesn*t match the labels in the figure*s legend, and also has a weird wording, making it unclear what (a) and (b) refer to. - In section 4 you say you test your models with FGSM accuracy, but in Figure 7 you report stepll and PGD accuracy, could you also plot the same curves for FGSM? - In Figure 4, I*m not sure I understand the right-tail of the distributions. Does it mean that when Delta_ij is very large, epsilon can be very small and still cause an adversarial pertubation? If so does it mean that overconfidence in the extreme is also bad?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Intriguing Properties of Adversarial Examples
It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples. In attempting to explain the origin of adversarial examples, previous studies have typically focused on the fact that neural networks operate on high dimensional data, they overfit, or they are too linear. Here we show that distributions of logit differences have a universal functional form. This functional form is independent of architecture, dataset, and training protocol; nor does it change during training. This leads to adversarial error having a universal scaling, as a power-law, with respect to the size of the adversarial perturbation. We show that this universality holds for a broad range of datasets (MNIST, CIFAR10, ImageNet, and random data), models (including state-of-the-art deep networks, linear models, adversarially trained networks, and networks trained on randomly shuffled labels), and attacks (FGSM, step l.l., PGD). Motivated by these results, we study the effects of reducing prediction entropy on adversarial robustness. Finally, we study the effect of network architectures on adversarial sensitivity. To do this, we use neural architecture search with reinforcement learning to find adversarially robust architectures on CIFAR10. Our resulting architecture is more robust to white emph{and} black box attacks compared to previous attempts.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 Very intriguing paper and results to say the least. I like the way it is written, and the neat interpretations that the authors give of what is going on (instead of assuming that readers will see the same). There is a well presented story of experiments to follow which gives us insight into the problem. Interesting insight into defensive distillation and the effects of uncertainty in neural networks. Quality/Clarity: well written and was easy for me to read Originality: Brings both new ideas and unexpected experimental results. Significance: Creates more questions than it answers, which imo is a positive as this topic definitely deserves more research. Remarks: - Maybe re-render Figure 3 at a higher resolution? - The caption of Figure 5 doesn*t match the labels in the figure*s legend, and also has a weird wording, making it unclear what (a) and (b) refer to. - In section 4 you say you test your models with FGSM accuracy, but in Figure 7 you report stepll and PGD accuracy, could you also plot the same curves for FGSM? - In Figure 4, I*m not sure I understand the right-tail of the distributions. Does it mean that when Delta_ij is very large, epsilon can be very small and still cause an adversarial pertubation? If so does it mean that overconfidence in the extreme is also bad?
assistant
 Very intriguing paper and results to say the least. I like the way it is written, and the neat interpretations that the authors give of what is going on (instead of assuming that readers will see the same). There is a well presented story of experiments to follow which gives us insight into the problem. Interesting insight into defensive distillation and the effects of uncertainty in neural networks. Quality/Clarity: well written and was easy for me to read Originality: Brings both new ideas and unexpected experimental results. Significance: Creates more questions than it answers, which imo is a positive as this topic definitely deserves more research. Remarks: - Maybe re-render Figure 3 at a higher resolution? - The caption of Figure 5 doesn*t match the labels in the figure*s legend, and also has a weird wording, making it unclear what (a) and (b) refer to. - In section 4 you say you test your models with FGSM accuracy, but in Figure 7 you report stepll and PGD accuracy, could you also plot the same curves for FGSM? - In Figure 4, I*m not sure I understand the right-tail of the distributions. Does it mean that when Delta_ij is very large, epsilon can be very small and still cause an adversarial pertubation? If so does it mean that overconfidence in the extreme is also bad?