Adversarial Robustness in Unsupervised Machine Learning: A Systematic
  Review
Mathias Lundteigen Mohus Jinyue Li
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.