From: ICML.cc/2021/Workshop/AML/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: This paper developed a method to attack objective detection models by a generative model. It can be further improved by addressing the reviewer's comments.

From: ICML.cc/2021/Workshop/AML/Paper86/Reviewer_c4mn
title: Review for generative adversarial attack on object detection
review: This paper develops a method to attack objective detection models by a generative model. The attack method uses a GAN to generate adversarial noise. The opimization process is done in the latent variable space. Authors also adapt early stopping method and two noise elimination methods.  The proposed method achieves higher transferability and invisibility compared to the baseline UEA method.

There are some quesitions unclear for me:

1. Compared to the baseline method, proposed method generates noise rather than the whole adversarial example. Apart from that,  loss function looks fairly similar to the baseline method. Is there any significant difference in loss function?  Whether the optimization object of generative models has big influence on transferability?
2. There should be a sketch of loss curve. Does early stopping improve the transferability by underfitting the current model? 

In summary, this paper manages to improve and speed up attack against objective detection. It would make contributions to the workshop.
rating: Accept
confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct