From: AAAI.org/2022/Workshop/AdvML/Program_Chairs
title: Paper Decision
decision: Accept (Oral)
comment: Both reviewers have high ratings on this paper. Thus it is accepted with an oral presentation.

From: AAAI.org/2022/Workshop/AdvML/Paper16/Reviewer_6hzb
title: Review for adversarial attacks with attention
review: This paper introduces a white box adversarial attack method using attention to refine the PGD attack. A GNN is trained on adversarial examples generated by PGD to narrow down the search space of PGD. The GNN is used in the attack algorithm to learn from possible 'bad' start points. Experiments show that the proposed method increase the attack efficiency and success rate compared to the baseline method. 
rating: 7: Good paper, accept
confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct

From: AAAI.org/2022/Workshop/AdvML/Paper16/Reviewer_aczm
title: A good idea to introduce GNN into adversarial attacks. The efficiency of regular adversarial attackers is improved.
review: Pros:
1. The writing is good.
2. The explanation of using GNN is clear.
3. The experimental results are convincing.
4. Using GNN as an attention mechanism to find the pitfalls of a classifier is a good idea. Maybe it can be used in more important scenarios.
rating: 8: Top 50% of accepted papers, clear accept
confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct