Evaluation Methodology for Attacks Against Confidence Thresholding Models
Current machine learning algorithms can be easily fooled by adversarial examples. One possible solution path is to make models that use confidence thresholding to avoid making mistakes. Such models refuse to make a prediction when they are not confident of their answer. We propose to evaluate such models in terms of tradeoff curves with the goal of high success rate on clean examples and low failure rate on adversarial examples. Existing untargeted attacks developed for models that do not use confidence thresholding tend to underestimate such models* vulnerability. We propose the MaxConfidence family of attacks, which are optimal in a variety of theoretical settings, including one realistic setting: attacks against linear models. Experiments show the attack attains good results in practice. We show that simple defenses are able to perform well on MNIST but not on CIFAR, contributing further to previous calls that MNIST should be retired as a benchmarking dataset for adversarial robustness research. We release code for these evaluations as part of the cleverhans (Papernot et al 2018) library (ICLR reviewers should be careful not to look at who contributed these features to cleverhans to avoid de-anonymizing this submission).
 This paper proposes an evaluation method for confidence thresholding defense models, as well as a new approach for generating of adversarial examples by choosing the wrong class with the most confidence when employing targeted attacks. Although the idea behind this paper is fairly simple, the paper is very difficult to understand. I have no idea that what is the propose of defining a new evaluation method and how this new evaluation method helps in the further design of the MaxConfidence method. Furthermore, the usage of the evaluation method unclear as well, it seems to be designed for evaluating the effectiveness of different adversarial attacks in Figure 2. However, in Figure 2, it is used for evaluating defense schemes. Again, this confuses me on what is the main topic of this paper. Indeed, why the commonly used attack success ratio or other similar measures cannot be used in the case? Intuitively, it should provide similar results to the success-failure curve. The paper also lacks experimental results, and the main conclusion from these results seems to be "MNIST is not suitable for benchmarking of adversarial attacks". If the authors claim that the proposed MaxConfidence attack method is more powerful than the MaxLoss based attacks, they should provide more comparisons between these methods. Meanwhile, the computational cost on large dataset such as ImageNet could be huge, the authors should further develop the method to make sure it works in all situations.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Evaluation Methodology for Attacks Against Confidence Thresholding Models
Current machine learning algorithms can be easily fooled by adversarial examples. One possible solution path is to make models that use confidence thresholding to avoid making mistakes. Such models refuse to make a prediction when they are not confident of their answer. We propose to evaluate such models in terms of tradeoff curves with the goal of high success rate on clean examples and low failure rate on adversarial examples. Existing untargeted attacks developed for models that do not use confidence thresholding tend to underestimate such models* vulnerability. We propose the MaxConfidence family of attacks, which are optimal in a variety of theoretical settings, including one realistic setting: attacks against linear models. Experiments show the attack attains good results in practice. We show that simple defenses are able to perform well on MNIST but not on CIFAR, contributing further to previous calls that MNIST should be retired as a benchmarking dataset for adversarial robustness research. We release code for these evaluations as part of the cleverhans (Papernot et al 2018) library (ICLR reviewers should be careful not to look at who contributed these features to cleverhans to avoid de-anonymizing this submission).
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 proposes an evaluation method for confidence thresholding defense models, as well as a new approach for generating of adversarial examples by choosing the wrong class with the most confidence when employing targeted attacks. Although the idea behind this paper is fairly simple, the paper is very difficult to understand. I have no idea that what is the propose of defining a new evaluation method and how this new evaluation method helps in the further design of the MaxConfidence method. Furthermore, the usage of the evaluation method unclear as well, it seems to be designed for evaluating the effectiveness of different adversarial attacks in Figure 2. However, in Figure 2, it is used for evaluating defense schemes. Again, this confuses me on what is the main topic of this paper. Indeed, why the commonly used attack success ratio or other similar measures cannot be used in the case? Intuitively, it should provide similar results to the success-failure curve. The paper also lacks experimental results, and the main conclusion from these results seems to be "MNIST is not suitable for benchmarking of adversarial attacks". If the authors claim that the proposed MaxConfidence attack method is more powerful than the MaxLoss based attacks, they should provide more comparisons between these methods. Meanwhile, the computational cost on large dataset such as ImageNet could be huge, the authors should further develop the method to make sure it works in all situations.
assistant
 This paper proposes an evaluation method for confidence thresholding defense models, as well as a new approach for generating of adversarial examples by choosing the wrong class with the most confidence when employing targeted attacks. Although the idea behind this paper is fairly simple, the paper is very difficult to understand. I have no idea that what is the propose of defining a new evaluation method and how this new evaluation method helps in the further design of the MaxConfidence method. Furthermore, the usage of the evaluation method unclear as well, it seems to be designed for evaluating the effectiveness of different adversarial attacks in Figure 2. However, in Figure 2, it is used for evaluating defense schemes. Again, this confuses me on what is the main topic of this paper. Indeed, why the commonly used attack success ratio or other similar measures cannot be used in the case? Intuitively, it should provide similar results to the success-failure curve. The paper also lacks experimental results, and the main conclusion from these results seems to be "MNIST is not suitable for benchmarking of adversarial attacks". If the authors claim that the proposed MaxConfidence attack method is more powerful than the MaxLoss based attacks, they should provide more comparisons between these methods. Meanwhile, the computational cost on large dataset such as ImageNet could be huge, the authors should further develop the method to make sure it works in all situations.