3 papers
cs.LG2026
One Framework for All: Cross-Modal Membership Inference for Generative Models
Dayong Ye, Tainqing Zhu, Kun Gao +6
Large generative models across text-to-text, text-to-image, and image-to-text modalities have been shown to pose significant privacy risks. One fundamental threat is membership inf…
cs.CR2026
Osmosis Distillation: Model Hijacking with the Fewest Samples
Yuchen Shi, Huajie Chen, Heng Xu +6
Transfer learning is devised to leverage knowledge from pre-trained models to solve new tasks with limited data and computational resources. Meanwhile, dataset distillation has eme…
cs.CR2024
Defending Against Neural Network Model Inversion Attacks via Data Poisoning
Shuai Zhou, Dayong Ye, Tianqing Zhu +1
Model inversion attacks pose a significant privacy threat to machine learning models by reconstructing sensitive data from their outputs. While various defenses have been proposed…