7 papers · 1 filter
SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Models
Zilan Wang, Junfeng Guo, Jiacheng Zhu +4
Recent advances in large-scale text-to-image (T2I) diffusion models have enabled a variety of downstream applications, including style customization, subject-driven personalization…
Semantic-Clipping: Efficient Vision-Language Modeling with Semantic-Guidedd Visual Selection
Bangzheng Li, Fei Wang, Wenxuan Zhou +5
Vision-Language Models (VLMs) leverage aligned visual encoders to transform images into visual tokens, allowing them to be processed similarly to text by the backbone large languag…
Making Every Step Effective: Jailbreaking Large Vision-Language Models Through Hierarchical KV Equalization
Shuyang Hao, Yiwei Wang, Bryan Hooi +4
In the realm of large vision-language models (LVLMs), adversarial jailbreak attacks serve as a red-teaming approach to identify safety vulnerabilities of these models and their ass…
Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset
Yingzi Ma, Jiongxiao Wang, Fei Wang +10
Machine unlearning has emerged as an effective strategy for forgetting specific information in the training data. However, with the increasing integration of visual data, privacy c…
From Introspection to Best Practices: Principled Analysis of Demonstrations in Multimodal In-Context Learning
Nan Xu, Fei Wang, Sheng Zhang +2
Motivated by in-context learning (ICL) capabilities of Large Language Models (LLMs), multimodal LLMs with additional visual modality are also exhibited with similar ICL abilities w…
Unraveling Cross-Modality Knowledge Conflicts in Large Vision-Language Models
Tinghui Zhu, Qin Liu, Fei Wang +2
Large Vision-Language Models (LVLMs) have demonstrated impressive capabilities for capturing and reasoning over multimodal inputs. However, these models are prone to parametric kno…