Model Inversion Robustness: Can Transfer Learning Help?

arXiv:2405.05588v1 Announce Type: cross
Abstract: Model Inversion (MI) attacks aim to reconstruct private training data by abusing access to machine learning models. Contemporary MI attacks have achieved impressive attack performance, posing serious threats to privacy. Meanwhile, all existing MI defense methods rely on regularization that is in direct conflict with the training objective, resulting in noticeable degradation in model utility. In this work, we take a different perspective, and propose a novel and simple Transfer Learning-based Defense against Model Inversion …

arxiv can cs.cr cs.cv cs.lg robustness transfer