Deep clustering integrates embedding and clustering together to obtain the optimal nonlinear embedding space, which is more effective in real-world scenarios compared with conventional clustering methods. However, the robustness of the clustering network is prone to being attenuated especially when it encounters an adversarial attack. A small perturbation in the embedding space will lead to diverse clustering results since the labels are absent. In this paper, we propose a robust deep clustering method based on adversarial learning. Specifically, we first attempt to define adversarial samples in the embedding space for the clustering network. Meanwhile, we devise an adversarial attack strategy to explore samples that easily fool the clustering layers but do not impact the performance of the deep embedding.

This supplementary material includes two sections i.e., details of baselines and descriptions of We select some samples with inconsistent clustering results before and after the attack strategy.

Clarity: The paper misses important details which makes some parts of the paper difficult to understand. Additionally, the clarity and quality of the writing could be improved; thorough proofreading is needed. Many notations were not introduced or are unclear: - There is no mention of what p and q exactly are in Section 2. It also seems to differ from the convention found in some papers that p is the true distribution and q is the variational distribution (e.g., in [12]). It is therefore expected that the roles of those quantities are clearly defined. Some experimental details also need clarifications: - What does the method denoted as "Conv" refer to? MIE and Graph were introduced in Section 5.4, but I could not find any description of Conv.

This paper presents a framework to improve the robustness of deep clustering to adversarial attack. This problem is important, and the method is sound and backed by extensive experimentation. However, the paper is in part not entirely clear and hard to reproduce with the current level of details, and a major rewriting is required to clarify the details of this method.

Deep clustering integrates embedding and clustering together to obtain the optimal nonlinear embedding space, which is more effective in real-world scenarios compared with conventional clustering methods. However, the robustness of the clustering network is prone to being attenuated especially when it encounters an adversarial attack. A small perturbation in the embedding space will lead to diverse clustering results since the labels are absent. In this paper, we propose a robust deep clustering method based on adversarial learning. Specifically, we first attempt to define adversarial samples in the embedding space for the clustering network. Meanwhile, we devise an adversarial attack strategy to explore samples that easily fool the clustering layers but do not impact the performance of the deep embedding.