Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models

2 June 2023

Papers citing "Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models"

7 / 7 papers shown

Title
Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey V. T. Truong Luan Ba Dang Long Bao Le DiffM MedIm 38 14 0 06 Aug 2024
White-box Membership Inference Attacks against Diffusion Models Yan Pang Tianhao Wang Xu Kang Mengdi Huai Yang Zhang AAML DiffM 18 22 0 11 Aug 2023
Can segmentation models be trained with fully synthetically generated data? Virginia Fernandez W. H. Pinaya Pedro Borges Petru-Daniel Tudosiu M. Graham Tom Kamiel Magda Vercauteren M. Jorge Cardoso DiffM MedIm 35 44 0 17 Sep 2022
Brain Imaging Generation with Latent Diffusion Models W. H. Pinaya Petru-Daniel Tudosiu J. Dafflon P. F. D. Costa Virginia Fernandez P. Nachev Sebastien Ourselin M. Jorge Cardoso DiffM MedIm 87 275 0 15 Sep 2022
Zero-Shot Text-to-Image Generation Aditya A. Ramesh Mikhail Pavlov Gabriel Goh Scott Gray Chelsea Voss Alec Radford Mark Chen Ilya Sutskever VLM 253 4,735 0 24 Feb 2021
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models Ahmed Alaa B. V. Breugel Evgeny S. Saveliev M. Schaar 38 135 0 17 Feb 2021
Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods James Jordon A. Wilson M. Schaar AI4CE 64 16 0 08 Dec 2020