Model Inversion Robustness: Can Transfer Learning Help?
Authors: Sy-Tuyen Ho, Koh Jun Hao, Keshigeyan Chandrasegaran, Ngoc-Bao Nguyen, Ngai-Man Cheung

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 (TL-DMI) to render MI-robust models. Particularly by leveraging TL we limit the number of layers encoding sensitive information from private training dataset thereby degrading the performance of MI attack. We conduct an analysis using Fisher Information to justify our method. Our defense is remarkably simple to implement. Without bells and whistles we show in extensive experiments that TL-DMI achieves state-of-the-art (SOTA) MI robustness. Our code pre-trained models demo and inverted data are available at: https://hosytuyen.github.io/projects/TL-DMI