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.
 A nice paper that clarifies the difference between the clean accuracy (accuracy of models on non-perturbed examples) and the robust accuracy (accuracy of models on adversarially perturbed examples) and it shows that changing the marginal distribution of the input data P(x) while preserving its semantic P(y|x) fixed affects the robustness of the model. Therefore, testing the robustness of the model should be performed in a careful manner. Comprehensive experiments were performed to show that changing the distribution of the MINST (smoothing) and CIFAR (saturation) data could lead to a significant difference in robust accuracy while the clean accuracy is almost steady. In addition, a set of experiments were performed in an attempt to search for the criteria required for choosing a proper dataset for testing adversarial attack to measure the robustness. Although I’m not expert in the field of adversarial attack but the paper is very nice to read and easy to follow (I have not checked the proof of the theorems though).
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>



 A nice paper that clarifies the difference between the clean accuracy (accuracy of models on non-perturbed examples) and the robust accuracy (accuracy of models on adversarially perturbed examples) and it shows that changing the marginal distribution of the input data P(x) while preserving its semantic P(y|x) fixed affects the robustness of the model. Therefore, testing the robustness of the model should be performed in a careful manner. Comprehensive experiments were performed to show that changing the distribution of the MINST (smoothing) and CIFAR (saturation) data could lead to a significant difference in robust accuracy while the clean accuracy is almost steady. In addition, a set of experiments were performed in an attempt to search for the criteria required for choosing a proper dataset for testing adversarial attack to measure the robustness. Although I’m not expert in the field of adversarial attack but the paper is very nice to read and easy to follow (I have not checked the proof of the theorems though).
assistant
 A nice paper that clarifies the difference between the clean accuracy (accuracy of models on non-perturbed examples) and the robust accuracy (accuracy of models on adversarially perturbed examples) and it shows that changing the marginal distribution of the input data P(x) while preserving its semantic P(y|x) fixed affects the robustness of the model. Therefore, testing the robustness of the model should be performed in a careful manner. Comprehensive experiments were performed to show that changing the distribution of the MINST (smoothing) and CIFAR (saturation) data could lead to a significant difference in robust accuracy while the clean accuracy is almost steady. In addition, a set of experiments were performed in an attempt to search for the criteria required for choosing a proper dataset for testing adversarial attack to measure the robustness. Although I’m not expert in the field of adversarial attack but the paper is very nice to read and easy to follow (I have not checked the proof of the theorems though).