3 papers
cs.CV2026
Enhancing Gradient Inversion Attacks in Federated Learning via Hierarchical Feature Optimization
Hao Fang, Wenbo Yu, Bin Chen +4
Federated Learning (FL) has emerged as a compelling paradigm for privacy-preserving distributed machine learning, allowing multiple clients to collaboratively train a global model…
cs.CV2026
ActErase: A Training-Free Paradigm for Precise Concept Erasure via Activation Redirection
Yi Sun, Xinhao Zhong, Hongyan Li +4
Recent advances in text-to-image diffusion models have demonstrated remarkable generation capabilities, yet they raise significant concerns regarding safety, copyright, and ethical…
cs.CV2025
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…