On the Sensitivity of Adversarial Robustness to Input Data Distributions
Neural networks are vulnerable to small adversarial perturbations. Existing literature largely focused on understanding and mitigating the vulnerability of learned models. In this paper, we demonstrate an intriguing phenomenon about the most popular robust training method in the literature, adversarial training: Adversarial robustness, unlike clean accuracy, is sensitive to the input data distribution. Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution. Our discovery of such sensitivity on data distribution is based on a study which disentangles the behaviors of clean accuracy and robust accuracy of the Bayes classifier. Empirical investigations further confirm our finding. We construct semantically-identical variants for MNIST and CIFAR10 respectively, and show that standardly trained models achieve comparable clean accuracies on them, but adversarially trained models achieve significantly different robustness accuracies. This counter-intuitive phenomenon indicates that input data distribution alone can affect the adversarial robustness of trained neural networks, not necessarily the tasks themselves. Lastly, we discuss the practical implications on evaluating adversarial robustness, and make initial attempts to understand this complex phenomenon.
 This paper provides several theoretical and practical insights on the impact of data distribution to adversarial robustness of trained networks. The paper reads well and provides analysis on two datasets MNIST and CIFAR10. I particularly like the result demonstrating that a lossless transformation on the data distribution could significantly impact the robustness of an adversarial trained models. The idea of using smoothness and saturation to bridge the gap between the MNIST and CIFAR10 datasets was also very interesting. One thing that is not clear from the paper is how one could use the findings from this paper and put it into practice. In other words, it would help if the authors could provide some insights on how to improve a model robustness w.r.t the changes in the data distribution. The authors did an attempt toward this in section 5, but that seems to only cover three factors that do not cause the difference in robustness.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
On the Sensitivity of Adversarial Robustness to Input Data Distributions
Neural networks are vulnerable to small adversarial perturbations. Existing literature largely focused on understanding and mitigating the vulnerability of learned models. In this paper, we demonstrate an intriguing phenomenon about the most popular robust training method in the literature, adversarial training: Adversarial robustness, unlike clean accuracy, is sensitive to the input data distribution. Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution. Our discovery of such sensitivity on data distribution is based on a study which disentangles the behaviors of clean accuracy and robust accuracy of the Bayes classifier. Empirical investigations further confirm our finding. We construct semantically-identical variants for MNIST and CIFAR10 respectively, and show that standardly trained models achieve comparable clean accuracies on them, but adversarially trained models achieve significantly different robustness accuracies. This counter-intuitive phenomenon indicates that input data distribution alone can affect the adversarial robustness of trained neural networks, not necessarily the tasks themselves. Lastly, we discuss the practical implications on evaluating adversarial robustness, and make initial attempts to understand this complex phenomenon.
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>



 This paper provides several theoretical and practical insights on the impact of data distribution to adversarial robustness of trained networks. The paper reads well and provides analysis on two datasets MNIST and CIFAR10. I particularly like the result demonstrating that a lossless transformation on the data distribution could significantly impact the robustness of an adversarial trained models. The idea of using smoothness and saturation to bridge the gap between the MNIST and CIFAR10 datasets was also very interesting. One thing that is not clear from the paper is how one could use the findings from this paper and put it into practice. In other words, it would help if the authors could provide some insights on how to improve a model robustness w.r.t the changes in the data distribution. The authors did an attempt toward this in section 5, but that seems to only cover three factors that do not cause the difference in robustness.
assistant
 This paper provides several theoretical and practical insights on the impact of data distribution to adversarial robustness of trained networks. The paper reads well and provides analysis on two datasets MNIST and CIFAR10. I particularly like the result demonstrating that a lossless transformation on the data distribution could significantly impact the robustness of an adversarial trained models. The idea of using smoothness and saturation to bridge the gap between the MNIST and CIFAR10 datasets was also very interesting. One thing that is not clear from the paper is how one could use the findings from this paper and put it into practice. In other words, it would help if the authors could provide some insights on how to improve a model robustness w.r.t the changes in the data distribution. The authors did an attempt toward this in section 5, but that seems to only cover three factors that do not cause the difference in robustness.