7 papers
DREAM: Dynamic Red-teaming across Environments for AI Models
Liming Lu, Xiang Gu, Junyu Huang +5
Large Language Models (LLMs) are increasingly used in agentic systems, where their interactions with diverse tools and environments create complex, multi-stage safety challenges. H…
Multimodal Robust Prompt Distillation for 3D Point Cloud Models
Xiang Gu, Liming Lu, Xu Zheng +3
Adversarial attacks pose a significant threat to learning-based 3D point cloud models, critically undermining their reliability in security-sensitive applications. Existing defense…
RoboView-Bias: Benchmarking Visual Bias in Embodied Agents for Robotic Manipulation
Enguang Liu, Siyuan Liang, Liming Lu +4
The safety and reliability of embodied agents rely on accurate and unbiased visual perception. However, existing benchmarks mainly emphasize generalization and robustness under per…
FERD: Fairness-Enhanced Data-Free Robustness Distillation
Zhengxiao Li, Liming Lu, Xu Zheng +4
Data-Free Robustness Distillation (DFRD) aims to transfer the robustness from the teacher to the student without accessing the training data. While existing methods focus on overal…
CIARD: Cyclic Iterative Adversarial Robustness Distillation
Liming Lu, Shuchao Pang, Xu Zheng +4
Adversarial robustness distillation (ARD) aims to transfer both performance and robustness from teacher model to lightweight student model, enabling resilient performance on resour…
Towards a 3D Transfer-based Black-box Attack via Critical Feature Guidance
Shuchao Pang, Zhenghan Chen, Shen Zhang +4
Deep neural networks for 3D point clouds have been demonstrated to be vulnerable to adversarial examples. Previous 3D adversarial attack methods often exploit certain information a…