Generative Adversarial Attack on Ensemble Clustering

Adversarial attack on learning tasks has attracted substantial attention in recent years; however, most existing works focus on supervised learning. Recently, research has shown that unsupervised learning, such as clustering, tends to be vulnerable due to adversarial attack. In this paper, we focus on a clustering algorithm widely used in the real-world environment, namely, ensemble clustering (EC). EC algorithms usually leverage basic partition (BP) and ensemble techniques to improve the clustering performance collaboratively. Each BP may stem from one trial of clustering, feature segment, or part of data stored on the cloud. We have observed that the attack tends to be less perceivable when only a few BPs are compromised. To explore plausible attack strategies, we propose a novel generative adversarial attack (GA2) model for EC, titled GA2EC. First, we show that not all BPs are equally important, and some of them are more vulnerable under adversarial attack. Second, we develop a generative adversarial model to mimic the attack on EC. In particular, the generative model will simulate behaviors of both clean BPs and perturbed key BPs, and their derived graphs, and thus can launch effective attacks with less attention. We have conducted extensive experiments on eleven clustering benchmarks and have demonstrated that our approach is effective in attacking EC under both transductive and inductive settings.