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cs.LG2026
Revisiting Model Inversion Evaluation: From Misleading Standards to Reliable Privacy Assessment
Sy-Tuyen Ho, Koh Jun Hao, Ngoc-Bao Nguyen +2
Model Inversion attacks aim to reconstruct information from private training data by exploiting access to a target model. Nearly all recent MI studies evaluate attack success using…
cs.LG2026
Do Vision-Language Models Leak What They Learn? Adaptive Token-Weighted Model Inversion Attacks
Ngoc-Bao Nguyen, Sy-Tuyen Ho, Koh Jun Hao +1
Model inversion (MI) attacks pose significant privacy risks by reconstructing private training data from trained neural networks. While prior studies have primarily examined unimod…
cs.LG2024
Model Inversion Robustness: Can Transfer Learning Help?
Sy-Tuyen Ho, Koh Jun Hao, Keshigeyan Chandrasegaran +2
Model Inversion (MI) attacks aim to reconstruct private training data by abusing access to machine learning models. Contemporary MI attacks have achieved impressive attack performa…