We propose a novel framework for avoiding the misclassification of data by using a framework of learning with rejection and adversarial examples. Recent developments in machine learning have opened new opportunities for industrial innovations such as self-driving cars. However, many machine learning models are vulnerable to adversarial attacks and industrial practitioners are concerned about accidents arising from misclassification. To avoid critical misclassifications, we define a sample that is likely to be mislabeled as a suspicious sample. Our main idea is to apply a framework of learning with rejection and adversarial examples to assist in the decision making for such suspicious samples. We propose two frameworks, learning with rejection under adversarial attacks and learning with protection. Learning with rejection under adversarial attacks is a naive extension of the learning with rejection framework for handling adversarial examples. Learning with protection is a practical application of learning with rejection under adversarial attacks. This algorithm transforms the original multi-class classification problem into a binary classification for a specific class, and we reject suspicious samples to protect a specific label. We demonstrate the effectiveness of the proposed method in experiments.
This paper wants to study the problem of “learning with rejection under adversarial attacks”. It first naively extends the learning with rejection framework for handling adversarial examples. It then considers the classical cost-sensitive learning by transfer the multi-class problem into binary classification problem through one-vs-all and using the technique they proposed to reject predictions on non-important labels, and name such technique as “learning with protection”. Finally, they do some experimental studies. 
The paper does not show any connection between “learning with rejection” and “adversarial learning”. The method it proposes is also a naïve extension of existing methods. Both the problem setting and the technique does not have novelty. The paper fails to realize that the motivated application is actually called “cost-sensitive learning” and has been studied long time before. The paper also has problems in writing. Finally, there is no comparison with any baseline. Only empirical results of the proposed methods are shown. Due to all these reasons, there is still a long way to go before the paper can be published. I will rate it a clear rejection.
More specially, 
The definition of “suspicious example” in Sec.3.1 has no relationship with adversary examples. Does the paper focus on adversary examples? If the definition has no relationship, it is classical learning with rejection. 
In the last equation of Page 3, there is no definition of \tilde L. Actually, according to Figure 1, x’s is more close to the decision boundary, it is an example more hard to classify, which could also be “suspicious”. 
In the definition of “suspicious example” at the beginning of Sec.3.1, is both x and x’ defined as suspicious examples in this way?
In the last equation of page 2, there is a rejection function, so minimizing this loss is a “separation-based approach”. However, at the end of Sec.2 the paper states they “follow a confidence-based approach”. Any comment on the inconsistency?
The motivated problem is not new. It is called cost-sensitive learning in machine learning and can date back to 2001:
Charles Elkan. The Foundations of Cost-Sensitive Learning. IJCAI 2001: 973-978.
Where they study the same problem when misclassifying one class of data may cost a lot than misclassifying another class of data. The current paper has not discussed any related work of cost-sensitive learning although they want to study a problem in its field. 
The paper should be also improved in writing in the following aspects. 
There is a lot of inaccurate statements in the paper. For example,  “In Sections 3 and 4, we propose and describe our algorithm”, what is the difference between propose and describe? “an estimator \hat h might return result that differ greatly from h^* in a case with finite samples”. Actually there are rigorous theoretical results describing how the number of finite samples will impact the estimator \hat h on unseen data. For example, 
Peter L. Bartlett, Shahar Mendelson. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. JMLR, 2002.
So inaccurate/unclear statements that will mislead readers should be avoided. 
In writing, the paper also lacks the necessary references in many places. For example, “Learning with rejection is a classification scenario where the learner is given the option to reject an instance instead of predicting its label.”, “…classifies adversary attacks to two types of attacks, white-box attack and black-box attack.”, “Methods for protecting against these adversarial examples are also being proposed.”. Necessary references are needed for these places.
The organization is also problematic. For example, in the second half of Sec.2 introducing two kinds of learning with rejection models, it should be included in a “related work” part. 
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Thank you for the rebuttal. I raised my score a little bit. But I still think this paper has not been ready to be published yet.