2 citations · 5 across the 18 of their papers we have counts for
11 papers · 1 filter
SilentDrift: Exploiting Action Chunking for Stealthy Backdoor Attacks on Vision-Language-Action Models
Bingxin Xu, Yuzhang Shang, Binghui Wang +1
Vision-Language-Action (VLA) models are increasingly deployed in safety-critical robotic applications, yet their security vulnerabilities remain underexplored. We identify a fundam…
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…
Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Nima Naderloui, Shenao Yan, Binghui Wang +4
Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning en…
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…