2 citations · 3 across the 15 of their papers we have counts for
10 papers
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…