Efficient Black-Box Adversarial Attack on Deep Clustering Models

Despite the significant progress made by deep clustering models in high-dimensional data processing, they remain vulnerable to adversarial examples. However, research on adversarial attacks against deep clustering algorithms appears to be relatively underexplored. To fill this gap, we propose a query-efficient black-box attack on deep clustering models, which leverages the transferability between different deep clustering models. Initially, we train a generator using a substitute deep clustering model, reducing the number of queries to the target model. Subsequently, when targeting an unknown deep clustering model, we employ the target query information to update both the substitute deep clustering model and the generator. Experimental evaluations on four state-of-the-art deep clustering models across three datasets demonstrate the efficacy of our method in disrupting clustering performance. The results indicate that our approach surpasses the performance of existing methods.