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/Paper17/Reviewer_m27H
title: Novel method for network verification
review: The author proposed a polytope traversing algorithm for network verification within a certain region. For Relu networks, the function is piecewise linear and decision regions are partitioned by many polytopes. By traversing these polytopes using a specific algorithm, we can verify a sample within a given region. I also have some concerns. What's the complexity of the method? Is it possible to scale it to larger datasets like ImageNet? I think the scalability is the major drawback of these deterministic verification approaches compared with probabilistic approaches like Randomized Smoothing. If this method can be further extended to larger-scale datasets, I think it will be a breakthrough.

rating: 7: Good paper, accept
confidence: 3: The reviewer is fairly confident that the evaluation is correct

From: AAAI.org/2022/Workshop/AdvML/Paper17/Reviewer_9CmL
title: A novel work for proposing polytope traversing algorithm
review: This paper points out that ReLU NNs will divide the input domain into many local polytopes. Based on this observation, this work develop a polytope traversing algorithm via BFS and apply in many aspects like local adversarial attacks. The idea is novel and exactly makes sense. 

However, I have some concerns about how this work can be applied to larger and more real tasks like image classification. First, the dimension of images is large, which may significantly increase the computational complexity of this algorithm. Second, since the sizes of different local polytopes may vary a lot, directly search polytopes via BFS may not find the optimal solution in some application like generating adversarial noises. Maybe the authors can attempt to test their methods in some image dataset like MNIST.
rating: 7: Good paper, accept
confidence: 3: The reviewer is fairly confident that the evaluation is correct