1 citations · 1 across the 4 of their papers we have counts for
5 papers
Defending against Patch-Based and Texture-Based Adversarial Attacks with Spectral Decomposition
Wei Zhang, Xinyu Chang, Xiao Li +2
Adversarial examples present significant challenges to the security of Deep Neural Network (DNN) applications. Specifically, there are patch-based and texture-based attacks that ar…
A Single Set of Adversarial Clothes Breaks Multiple Defense Methods in the Physical World
Wei Zhang, Zhanhao Hu, Xiao Li +2
In recent years, adversarial attacks against deep learning-based object detectors in the physical world have attracted much attention. To defend against these attacks, researchers…
PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection
Xiao Li, Yiming Zhu, Yifan Huang +4
Object detection plays a crucial role in many security-sensitive applications. However, several recent studies have shown that object detectors can be easily fooled by physically r…
Faster-GCG: Efficient Discrete Optimization Jailbreak Attacks against Aligned Large Language Models
Xiao Li, Wei Zhang, Zhuhong Li +6
Aligned Large Language Models (LLMs) have attracted significant attention for their safety, particularly in the context of jailbreak attacks that attempt to bypass guardrails via a…
Perfect Gradient Inversion in Federated Learning: A New Paradigm from the Hidden Subset Sum Problem
Qiongxiu Li, Lixia Luo, Agnese Gini +6
Federated Learning (FL) has emerged as a popular paradigm for collaborative learning among multiple parties. It is considered privacy-friendly because local data remains on persona…