6 papers
MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models
Wenyi Hong, Yean Cheng, Zhuoyi Yang +6
In recent years, vision language models (VLMs) have made significant advancements in video understanding. However, a crucial capability - fine-grained motion comprehension - remain…
LLM-Agnostic Semantic Representation Attack
Jiawei Lian, Jianhong Pan, Lefan Wang +4
Large Language Models (LLMs) increasingly employ alignment techniques to prevent harmful outputs. Despite these safeguards, attackers can circumvent them by crafting adversarial pr…
Semantic Representation Attack against Aligned Large Language Models
Jiawei Lian, Jianhong Pan, Lefan Wang +3
Large Language Models (LLMs) increasingly employ alignment techniques to prevent harmful outputs. Despite these safeguards, attackers can circumvent them by crafting prompts that i…
Attack Anything: Blind DNNs via Universal Background Adversarial Attack
Jiawei Lian, Shaohui Mei, Xiaofei Wang +5
It has been widely substantiated that deep neural networks (DNNs) are susceptible and vulnerable to adversarial perturbations. Existing studies mainly focus on performing attacks b…
Revealing the Intrinsic Ethical Vulnerability of Aligned Large Language Models
Jiawei Lian, Jianhong Pan, Lefan Wang +3
Large language models (LLMs) are foundational explorations to artificial general intelligence, yet their alignment with human values via instruction tuning and preference learning…
PADetBench: Towards Benchmarking Physical Attacks against Object Detection
Jiawei Lian, Jianhong Pan, Lefan Wang +3
Physical attacks against object detection have gained increasing attention due to their significant practical implications. However, conducting physical experiments is extremely ti…