activity
20222026
most citedA2: Efficient Automated Attacker for Boosting Adversarial Training

8 citations · 16 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2026

How Reliable Are System-One Decisions in Medicine? A Comparative Study of Jev, Jev-like Models, and LLMs

Yanrui Du, Danyang Zhao, Sendong Zhao +6

Jev has attracted considerable attention for its rapid, generalizable decision-making, yet its medical reliability remains underexplored. To address this gap, our study compares va…

cs.AI2025

UpSafeC: Upcycling for Controllable Safety in Large Language Models

Yuhao Sun, Zhuoer Xu, Shiwen Cui +4

Large Language Models (LLMs) have achieved remarkable progress across a wide range of tasks, but remain vulnerable to safety risks such as harmful content generation and jailbreak…

cs.AI2024

TroubleLLM: Align to Red Team Expert

Zhuoer Xu, Jianping Zhang, Shiwen Cui +2

Large Language Models (LLMs) become the start-of-the-art solutions for a variety of natural language tasks and are integrated into real-world applications. However, LLMs can be pot…

cs.CV2023★ 2 cited

Backpropagation Path Search On Adversarial Transferability

Zhuoer Xu, Zhangxuan Gu, Jianping Zhang +3

Deep neural networks are vulnerable to adversarial examples, dictating the imperativeness to test the model's robustness before deployment. Transfer-based attackers craft adversari…

cs.CV2023★ 6 cited

On the Robustness of Latent Diffusion Models

Jianping Zhang, Zhuoer Xu, Shiwen Cui +3

Latent diffusion models achieve state-of-the-art performance on a variety of generative tasks, such as image synthesis and image editing. However, the robustness of latent diffusio…

cs.CV2022★ 8 cited

A2: Efficient Automated Attacker for Boosting Adversarial Training

Zhuoer Xu, Guanghui Zhu, Changhua Meng +5

Based on the significant improvement of model robustness by AT (Adversarial Training), various variants have been proposed to further boost the performance. Well-recognized methods…