8 citations · 13 across the 4 of their papers we have counts for
4 papers
Ownership Protection of Generative Adversarial Networks
Hailong Hu, Jun Pang
Generative adversarial networks (GANs) have shown remarkable success in image synthesis, making GAN models themselves commercially valuable to legitimate model owners. Therefore, i…
PriSampler: Mitigating Property Inference of Diffusion Models
Hailong Hu, Jun Pang
Diffusion models have been remarkably successful in data synthesis. However, when these models are applied to sensitive datasets, such as banking and human face data, they might br…
Membership Inference of Diffusion Models
Hailong Hu, Jun Pang
Recent years have witnessed the tremendous success of diffusion models in data synthesis. However, when diffusion models are applied to sensitive data, they also give rise to sever…
Model Extraction and Defenses on Generative Adversarial Networks
Hailong Hu, Jun Pang
Model extraction attacks aim to duplicate a machine learning model through query access to a target model. Early studies mainly focus on discriminative models. Despite the success,…