As the adoption of machine learning models increases, ensuring robust models against adversarial attacks is increasingly important. With unsupervised machine learning gaining more attention, ensuring it is robust against attacks is vital. This paper conducts a systematic literature review on the robustness of unsupervised learning, collecting 86 papers. Our results show that most research focuses on privacy attacks, which have effective defenses; however, many attacks lack effective and general defensive measures. Based on the results, we formulate a model on the properties of an attack on unsupervised learning, contributing to future research by providing a model to use.

翻译：随着机器学习模型的广泛应用，确保模型对对抗攻击具有鲁棒性日益重要。随着无监督机器学习获得更多关注，确保其抵御攻击的鲁棒性至关重要。本文对无监督学习的鲁棒性进行了系统性文献综述，共收集86篇论文。研究结果表明，大部分研究聚焦于隐私攻击，这类攻击已有有效防御措施；然而，许多攻击仍缺乏有效且通用的防御手段。基于研究结果，我们构建了针对无监督学习攻击属性的模型，为未来研究提供了可用的模型框架。