1 citations · 1 across the 10 of their papers we have counts for
8 papers · 1 filter
GeoCloak: An Agentic Framework for Semantics-Preserving Geolocation Privacy via Peripheral Clue Manipulation
Yixin Wu, Mingjie Li, Yun Shen +2
The rapid advancement of large vision language models (LVLMs) has made geolocation inference from images highly accurate and accessible. This poses serious privacy risks: a passive…
GEO-Flag: Detecting and Measuring GEO-Optimized Web Content
Junjie Chu, Ye Leng, Mingjie Li +3
Generative Engine Optimization (GEO) modifies web content to increase its likelihood of being selected and cited by generative search engines. This can give strategically optimized…
Innocent Panels, Hateful Stories: Evaluating and Detecting Hateful Intent in Multi-Turn Visual Story Generation
Ye Leng, Junjie Chu, Yiting Qu +3
Picture books and comics have long been used to disseminate hateful narratives because they are easily understood even by children, as exemplified by the notorious Nazi propaganda…
Not All Tokens Matter Equally: Dynamic In-context Vector Distillation with Decisive-Token Supervision for Long-form Medical Report Generation
Ning Wu, Rui Liu, Xinkun Lin +5
Distilling demonstration effects into hidden-space interventions offers a lightweight alternative to full finetuning. However, existing multimodal variants are mostly evaluated on…
A Systematic Study of Training-Free Methods for Trustworthy Large Language Models
Wai Man Si, Mingjie Li, Michael Backes +1
As Large Language Models (LLMs) receive increasing attention and are being deployed across various domains, their potential risks, including generating harmful or biased content, p…
Pruning Unsafe Tickets: A Resource-Efficient Framework for Safer and More Robust LLMs
Wai Man Si, Mingjie Li, Michael Backes +1
Machine learning models are increasingly deployed in real-world applications, but even aligned models such as Mistral and LLaVA still exhibit unsafe behaviors inherited from pre-tr…
When Understanding Becomes a Risk: Authenticity and Safety Risks in the Emerging Image Generation Paradigm
Ye Leng, Junjie Chu, Mingjie Li +5
Recently, multimodal large language models (MLLMs) have emerged as a unified paradigm for language and image generation. Compared with diffusion models, MLLMs possess a much strong…
Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMs
Yukun Jiang, Hai Huang, Mingjie Li +3
By introducing routers to selectively activate experts in Transformer layers, the mixture-of-experts (MoE) architecture significantly reduces computational costs in large language…