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).
 The paper presents an evaluation methodology for evaluating attacks on confidence thresholding methods and proposes a new kind of attack. In general I find the writing poor, as it is not exactly clear what the focus of the paper is - the evaluation or the new attack? The experiments lacking and the proposed evaluation methodology & theoretical guarantees trivial. Major remarks: - Linking the code and asking the reviewers not to look seems like bad practice and close to violating double blind, especially when considering that the cleavhans library is well known. Should have just removed the link and cleavhans name and state it will be released after review. - It is unclear what the focus of the paper is, is it the evaluation methodology or the new attack? While the evaluation methodology is presented as the main topic in title, abstract and introduction most of the paper is dedicated to the attack. - The evaluation methodology is a good idea but is quiet trivial. Also, curves are nice visually but hard to compare between close competitors. A numeric value like area-under-the-curve should be better. - The theoretical guarantees is also quiet trivial, more or less saying that if a confident adversarial attack exists then finding the most confident attack will be successful. Besides that the third part of the proof can be simplified significantly. - The experiments are very lacking. The authors do not compare to any other attack so there is no way to evaluate the significance of their proposed method - That being said, the max-confidence attack by itself sounds interesting, and might be useful even outside confidence thresholding. - One interesting base-line experiment could be trying this attack on re-calibrated networks e.g. “On Calibration of Modern Neural Networks” - Another baseline for comparison could be doing just a targeted attack with highest probability wrong class. - I found part 4.2 unclear - In the conclusion, the first and last claims are not supported by the text in my mind. Minor remarks: - The abstract isn’t clear jumping from one topic to the next in the middle without any connection. - Having Fig.1 and 2 right on the start is a bit annoying, would be better to put in the relevant spot and after the terms have been introduced. -In 6.2 the periodically in third line from the end seems out of place.
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>



 The paper presents an evaluation methodology for evaluating attacks on confidence thresholding methods and proposes a new kind of attack. In general I find the writing poor, as it is not exactly clear what the focus of the paper is - the evaluation or the new attack? The experiments lacking and the proposed evaluation methodology & theoretical guarantees trivial. Major remarks: - Linking the code and asking the reviewers not to look seems like bad practice and close to violating double blind, especially when considering that the cleavhans library is well known. Should have just removed the link and cleavhans name and state it will be released after review. - It is unclear what the focus of the paper is, is it the evaluation methodology or the new attack? While the evaluation methodology is presented as the main topic in title, abstract and introduction most of the paper is dedicated to the attack. - The evaluation methodology is a good idea but is quiet trivial. Also, curves are nice visually but hard to compare between close competitors. A numeric value like area-under-the-curve should be better. - The theoretical guarantees is also quiet trivial, more or less saying that if a confident adversarial attack exists then finding the most confident attack will be successful. Besides that the third part of the proof can be simplified significantly. - The experiments are very lacking. The authors do not compare to any other attack so there is no way to evaluate the significance of their proposed method - That being said, the max-confidence attack by itself sounds interesting, and might be useful even outside confidence thresholding. - One interesting base-line experiment could be trying this attack on re-calibrated networks e.g. “On Calibration of Modern Neural Networks” - Another baseline for comparison could be doing just a targeted attack with highest probability wrong class. - I found part 4.2 unclear - In the conclusion, the first and last claims are not supported by the text in my mind. Minor remarks: - The abstract isn’t clear jumping from one topic to the next in the middle without any connection. - Having Fig.1 and 2 right on the start is a bit annoying, would be better to put in the relevant spot and after the terms have been introduced. -In 6.2 the periodically in third line from the end seems out of place.
assistant
 The paper presents an evaluation methodology for evaluating attacks on confidence thresholding methods and proposes a new kind of attack. In general I find the writing poor, as it is not exactly clear what the focus of the paper is - the evaluation or the new attack? The experiments lacking and the proposed evaluation methodology & theoretical guarantees trivial. Major remarks: - Linking the code and asking the reviewers not to look seems like bad practice and close to violating double blind, especially when considering that the cleavhans library is well known. Should have just removed the link and cleavhans name and state it will be released after review. - It is unclear what the focus of the paper is, is it the evaluation methodology or the new attack? While the evaluation methodology is presented as the main topic in title, abstract and introduction most of the paper is dedicated to the attack. - The evaluation methodology is a good idea but is quiet trivial. Also, curves are nice visually but hard to compare between close competitors. A numeric value like area-under-the-curve should be better. - The theoretical guarantees is also quiet trivial, more or less saying that if a confident adversarial attack exists then finding the most confident attack will be successful. Besides that the third part of the proof can be simplified significantly. - The experiments are very lacking. The authors do not compare to any other attack so there is no way to evaluate the significance of their proposed method - That being said, the max-confidence attack by itself sounds interesting, and might be useful even outside confidence thresholding. - One interesting base-line experiment could be trying this attack on re-calibrated networks e.g. “On Calibration of Modern Neural Networks” - Another baseline for comparison could be doing just a targeted attack with highest probability wrong class. - I found part 4.2 unclear - In the conclusion, the first and last claims are not supported by the text in my mind. Minor remarks: - The abstract isn’t clear jumping from one topic to the next in the middle without any connection. - Having Fig.1 and 2 right on the start is a bit annoying, would be better to put in the relevant spot and after the terms have been introduced. -In 6.2 the periodically in third line from the end seems out of place.