Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and
  Defenses
Hao Fang Yixiang Qiu Hongyao Yu Wenbo Yu Jiawei Kong Baoli Chong Bin Chen Xuan Wang Shu-Tao Xia
Model Inversion (MI) attacks aim to disclose private information about the
training data by abusing access to the pre-trained models. These attacks enable
adversaries to reconstruct high-fidelity data that closely aligns with the
private training data, which has raised significant privacy concerns. Despite
the rapid advances in the field, we lack a comprehensive overview of existing
MI attacks and defenses. To fill this gap, this paper thoroughly investigates
this field and presents a holistic survey. Firstly, our work briefly reviews
the traditional MI on machine learning scenarios. We then elaborately analyze
and compare numerous recent attacks and defenses on \textbf{D}eep
\textbf{N}eural \textbf{N}etworks (DNNs) across multiple modalities and
learning tasks.