19 citations · 41 across the 17 of their papers we have counts for
17 papers
DualOptim+: Bridging Shared and Decoupled Optimizer States for Better Machine Unlearning in Large Language Models
Xuyang Zhong, Qizhang Li, Yiwen Guo +1
We propose DualOptim+, a novel optimization framework for improving machine unlearning in large language models. It introduces a base state to capture common representations shared…
Grounding-MD: Grounded Video-language Pre-training for Open-World Moment Detection
Weijun Zhuang, Qizhang Li, Xin Li +5
Temporal Action Detection and Moment Retrieval constitute two pivotal tasks in video understanding, focusing on precisely localizing temporal segments corresponding to specific act…
Triad: Empowering LMM-based Anomaly Detection with Vision Expert-guided Visual Tokenizer and Manufacturing Process
Yuanze Li, Shihao Yuan, Haolin Wang +5
Although recent methods have tried to introduce large multimodal models (LMMs) into industrial anomaly detection (IAD), their generalization in the IAD field is far inferior to tha…
Deciphering the Chaos: Enhancing Jailbreak Attacks via Adversarial Prompt Translation
Qizhang Li, Xiaochen Yang, Wangmeng Zuo +1
Automatic adversarial prompt generation provides remarkable success in jailbreaking safely-aligned large language models (LLMs). Existing gradient-based attacks, while demonstratin…
Improved Generation of Adversarial Examples Against Safety-aligned LLMs
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
Adversarial prompts generated using gradient-based methods exhibit outstanding performance in performing automatic jailbreak attacks against safety-aligned LLMs. Nevertheless, due…
Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
The adversarial vulnerability of deep neural networks (DNNs) has drawn great attention due to the security risk of applying these models in real-world applications. Based on transf…
DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection
Zehao Wang, Yiwen Guo, Qizhang Li +2
Data augmentation is a dominant method for reducing model overfitting and improving generalization. Most existing data augmentation methods tend to find a compromise in augmenting…
Improving Transferability of Adversarial Examples via Bayesian Attacks
Qizhang Li, Yiwen Guo, Xiaochen Yang +2
The transferability of adversarial examples allows for the attack on unknown deep neural networks (DNNs), posing a serious threat to many applications and attracting great attentio…
Improving Adversarial Transferability via Intermediate-level Perturbation Decay
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
Intermediate-level attacks that attempt to perturb feature representations following an adversarial direction drastically have shown favorable performance in crafting transferable…
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
The transferability of adversarial examples across deep neural networks (DNNs) is the crux of many black-box attacks. Many prior efforts have been devoted to improving the transfer…
Adversarial Contrastive Learning via Asymmetric InfoNCE
Qiying Yu, Jieming Lou, Xianyuan Zhan +4
Contrastive learning (CL) has recently been applied to adversarial learning tasks. Such practice considers adversarial samples as additional positive views of an instance, and by m…
Squeeze Training for Adversarial Robustness
Qizhang Li, Yiwen Guo, Wangmeng Zuo +1
The vulnerability of deep neural networks (DNNs) to adversarial examples has attracted great attention in the machine learning community. The problem is related to non-flatness and…
On Steering Multi-Annotations per Sample for Multi-Task Learning
Yuanze Li, Yiwen Guo, Qizhang Li +2
The study of multi-task learning has drawn great attention from the community. Despite the remarkable progress, the challenge of optimally learning different tasks simultaneously r…
An Intermediate-level Attack Framework on The Basis of Linear Regression
Yiwen Guo, Qizhang Li, Wangmeng Zuo +1
This paper substantially extends our work published at ECCV, in which an intermediate-level attack was proposed to improve the transferability of some baseline adversarial examples…
Backpropagating Linearly Improves Transferability of Adversarial Examples
Yiwen Guo, Qizhang Li, Hao Chen
The vulnerability of deep neural networks (DNNs) to adversarial examples has drawn great attention from the community. In this paper, we study the transferability of such examples,…
Practical No-box Adversarial Attacks against DNNs
Qizhang Li, Yiwen Guo, Hao Chen
The study of adversarial vulnerabilities of deep neural networks (DNNs) has progressed rapidly. Existing attacks require either internal access (to the architecture, parameters, or…
Yet Another Intermediate-Level Attack
Qizhang Li, Yiwen Guo, Hao Chen
The transferability of adversarial examples across deep neural network (DNN) models is the crux of a spectrum of black-box attacks. In this paper, we propose a novel method to enha…