From: AAAI.org/2022/Workshop/AdvML/Program_Chairs
title: Paper Decision
decision: Accept (Long Paper)
comment: Both reviewers agree to accept this paper. Please address their comments in the final version.

From: AAAI.org/2022/Workshop/AdvML/Paper21/Reviewer_29so
title: An effective voxel-drop-based certified defense method on PointNet.
review: Pros:

1, The math is good, and the equations are clear.

2, The intuition is direct.

3, The paper is easy to follow.

Cons:

1, The number of comparison methods are not enough.

2, It will be better if a pipeline figure is added into the paper, which shows how the defense works.

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/Paper21/Reviewer_VaXN
title: Review of the submission
review: This paper proposes a provable defense based on randomized smoothing for certifying robustness against clustering attacks on 3D point clouds.

Strengths:
- The motivation of using the random sampling strategy is clear.
- The theoretical results are provided.
- Some experiments can demonstrate the effectiveness of the method.

I also have some concerns about this work:
- Is it the first work to study provable defenses of 3D classifiers? If not, please discuss with previous work.
- Can you show the certified robustness in experiments?

Overall, I think this work is interesting and makes a contribution to the field. 
rating: 7: Good paper, accept
confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct