Random Erasing vs. Model Inversion: A Promising Defense or a False Hope? 事件

Random Erasing vs. Model Inversion: A Promising Defense or a False Hope? arXiv:2409.01062v4 Announce Type: replace-cross Abstract: Model Inversion (MI) attacks pose a significant privacy threat by reconstructing private training data from machine learning models. While existing defenses primarily concentrate on model-centric approaches, the impact of data on MI robustness remains largely unexplored. In this work, we explore Random Erasing (RE), a technique traditionally used for improving model