7 citations · 12 across the 9 of their papers we have counts for
4 papers · 1 filter
Rank Matters: Understanding and Defending Model Inversion Attacks via Low-Rank Feature Filtering
Hongyao Yu, Yixiang Qiu, Hao Fang +6
Model Inversion Attacks (MIAs) pose a significant threat to data privacy by reconstructing sensitive training samples from the knowledge embedded in trained machine learning models…
MIBench: A Comprehensive Framework for Benchmarking Model Inversion Attack and Defense
Yixiang Qiu, Hongyao Yu, Hao Fang +6
Model Inversion (MI) attacks aim at leveraging the output information of target models to reconstruct privacy-sensitive training data, raising critical concerns regarding the priva…
A Closer Look at GAN Priors: Exploiting Intermediate Features for Enhanced Model Inversion Attacks
Yixiang Qiu, Hao Fang, Hongyao Yu +3
Model Inversion (MI) attacks aim to reconstruct privacy-sensitive training data from released models by utilizing output information, raising extensive concerns about the security…
Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses
Hao Fang, Yixiang Qiu, Hongyao Yu +7
Deep Neural Networks (DNNs) have revolutionized various domains with their exceptional performance across numerous applications. However, Model Inversion (MI) attacks, which disclo…