5 citations · 5 across the 3 of their papers we have counts for
Showing 2023Show all
3 papers · 1 filter
cs.CR2023
SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models
Boyang Zhang, Zheng Li, Ziqing Yang +4
While advanced machine learning (ML) models are deployed in numerous real-world applications, previous works demonstrate these models have security and privacy vulnerabilities. Var…
cs.CR2023
A Comprehensive Study of Privacy Risks in Curriculum Learning
Joann Qiongna Chen, Xinlei He, Zheng Li +2
Training a machine learning model with data following a meaningful order, i.e., from easy to hard, has been proven to be effective in accelerating the training process and achievin…
cs.CV2023★ 5 cited
Pseudo Label-Guided Model Inversion Attack via Conditional Generative Adversarial Network
Xiaojian Yuan, Kejiang Chen, Jie Zhang +3
Model inversion (MI) attacks have raised increasing concerns about privacy, which can reconstruct training data from public models. Indeed, MI attacks can be formalized as an optim…