Improved robustness to adversarial examples using Lipschitz regularization of the loss
We augment adversarial training (AT) with worst case adversarial training (WCAT) which improves adversarial robustness by 11% over the current state- of-the-art result in the `2-norm on CIFAR-10. We interpret adversarial training as Total Variation Regularization, which is a fundamental tool in mathematical im- age processing, and WCAT as Lipschitz regularization, which appears in Image Inpainting. We obtain verifiable worst and average case robustness guarantees, based on the expected and maximum values of the norm of the gradient of the loss.
 The authors propose a novel method of training neural networks for robustness of adversarial attacks based on 2-norm and Lipschitz regularization. Unfortunately I*m not at all familiar with the literature on adversarial attacks so it is difficult for me to judge the quality and significance of this work. The theoretical results look plausible and clearly stated. The experiments show improvements over existing methods but I can*t tell whether the right baselines were used. Overall the writing is reasonably clear but not very accessible for someone not already familiar with the area.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Improved robustness to adversarial examples using Lipschitz regularization of the loss
We augment adversarial training (AT) with worst case adversarial training (WCAT) which improves adversarial robustness by 11% over the current state- of-the-art result in the `2-norm on CIFAR-10. We interpret adversarial training as Total Variation Regularization, which is a fundamental tool in mathematical im- age processing, and WCAT as Lipschitz regularization, which appears in Image Inpainting. We obtain verifiable worst and average case robustness guarantees, based on the expected and maximum values of the norm of the gradient of the loss.
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>



 The authors propose a novel method of training neural networks for robustness of adversarial attacks based on 2-norm and Lipschitz regularization. Unfortunately I*m not at all familiar with the literature on adversarial attacks so it is difficult for me to judge the quality and significance of this work. The theoretical results look plausible and clearly stated. The experiments show improvements over existing methods but I can*t tell whether the right baselines were used. Overall the writing is reasonably clear but not very accessible for someone not already familiar with the area.
assistant
 The authors propose a novel method of training neural networks for robustness of adversarial attacks based on 2-norm and Lipschitz regularization. Unfortunately I*m not at all familiar with the literature on adversarial attacks so it is difficult for me to judge the quality and significance of this work. The theoretical results look plausible and clearly stated. The experiments show improvements over existing methods but I can*t tell whether the right baselines were used. Overall the writing is reasonably clear but not very accessible for someone not already familiar with the area.