2 citations · 5 across the 18 of their papers we have counts for
19 papers
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…
Towards Strong Certified Defense with Universal Asymmetric Randomization
Hanbin Hong, Ashish Kundu, Ali Payani +2
Randomized smoothing has become essential for achieving certified adversarial robustness in machine learning models. However, current methods primarily use isotropic noise distribu…
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…
On the Inference (In-)Security of Vertical Federated Learning: Efficient Auditing against Inference Tampering Attack
Chung-ju Huang, Ziqi Zhang, Yinggui Wang +3
Vertical Federated Learning (VFL) is an emerging distributed learning paradigm for cross-silo collaboration without accessing participants' data. However, existing VFL work lacks a…
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…
GenoArmory: A Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models
Haozheng Luo, Chenghao Qiu, Yimin Wang +9
We propose the first unified adversarial attack benchmark for Genomic Foundation Models (GFMs), named GenoArmory. Unlike existing GFM benchmarks, GenoArmory offers the first compre…
FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning
Binghui Zhang, Luis Mares De La Cruz, Binghui Wang
Federated Learning (FL) is an emerging decentralized learning paradigm that can partly address the privacy concern that cannot be handled by traditional centralized and distributed…
Backdoor Attacks on Discrete Graph Diffusion Models
Jiawen Wang, Samin Karim, Samin Bin Karim +2
Diffusion models have demonstrated remarkable generative capabilities in continuous data domains such as images and videos. Recently, discrete graph diffusion models (DGDMs) have e…
Learning Robust and Privacy-Preserving Representations via Information Theory
Binghui Zhang, Sayedeh Leila Noorbakhsh, Yun Dong +2
Machine learning models are vulnerable to both security attacks (e.g., adversarial examples) and privacy attacks (e.g., private attribute inference). We take the first step to miti…
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…
Efficient Byzantine-Robust and Provably Privacy-Preserving Federated Learning
Chenfei Nie, Qiang Li, Yuxin Yang +2
Federated learning (FL) is an emerging distributed learning paradigm without sharing participating clients' private data. However, existing works show that FL is vulnerable to both…
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…
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence
Shuya Feng, Meisam Mohammady, Hanbin Hong +4
Differentially private federated learning (DP-FL) is a promising technique for collaborative model training while ensuring provable privacy for clients. However, optimizing the tra…
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…
Inf2Guard: An Information-Theoretic Framework for Learning Privacy-Preserving Representations against Inference Attacks
Sayedeh Leila Noorbakhsh, Binghui Zhang, Yuan Hong +1
Machine learning (ML) is vulnerable to inference (e.g., membership inference, property inference, and data reconstruction) attacks that aim to infer the private information of trai…
Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning Against Attribute Inference Attacks
Caridad Arroyo Arevalo, Sayedeh Leila Noorbakhsh, Yun Dong +2
Federated learning (FL) has been widely studied recently due to its property to collaboratively train data from different devices without sharing the raw data. Nevertheless, recent…
DeepTheft: Stealing DNN Model Architectures through Power Side Channel
Yansong Gao, Huming Qiu, Zhi Zhang +6
Deep Neural Network (DNN) models are often deployed in resource-sharing clouds as Machine Learning as a Service (MLaaS) to provide inference services.To steal model architectures t…
Certifiable Black-Box Attacks with Randomized Adversarial Examples: Breaking Defenses with Provable Confidence
Hanbin Hong, Xinyu Zhang, Binghui Wang +2
Black-box adversarial attacks have demonstrated strong potential to compromise machine learning models by iteratively querying the target model or leveraging transferability from a…