6 papers
Practical, Generalizable and Robust Backdoor Attacks on Text-to-Image Diffusion Models
Haoran Dai, Jiawen Wang, Ruo Yang +4
Text-to-image diffusion models (T2I DMs) have achieved remarkable success in generating high-quality and diverse images from text prompts, yet recent studies have revealed their vu…
Backdoor Attacks on Discrete Graph Diffusion Models
Jiawen Wang, Samin Karim, Yuan Hong +1
Diffusion models are powerful generative models in continuous data domains such as image and video data. Discrete graph diffusion models (DGDMs) have recently extended them for gra…
FedGMark: Certifiably Robust Watermarking for Federated Graph Learning
Yuxin Yang, Qiang Li, Yuan Hong +1
Federated graph learning (FedGL) is an emerging learning paradigm to collaboratively train graph data from various clients. However, during the development and deployment of FedGL…
Understanding Data Reconstruction Leakage in Federated Learning from a Theoretical Perspective
Zifan Wang, Binghui Zhang, Meng Pang +2
Federated learning (FL) is an emerging collaborative learning paradigm that aims to protect data privacy. Unfortunately, recent works show FL algorithms are vulnerable to the serio…
A Learning-Based Attack Framework to Break SOTA Poisoning Defenses in Federated Learning
Yuxin Yang, Qiang Li, Chenfei Nie +3
Federated Learning (FL) is a novel client-server distributed learning framework that can protect data privacy. However, recent works show that FL is vulnerable to poisoning attacks…
Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses
Yuxin Yang, Qiang Li, Jinyuan Jia +2
Federated graph learning (FedGL) is an emerging federated learning (FL) framework that extends FL to learn graph data from diverse sources. FL for non-graph data has shown to be vu…