2 citations · 3 across the 6 of their papers we have counts for
7 papers
STRATA: Self-Maintaining Structured Experience Graphs for Self-Evolving Language Agents
Guanhua Zhu, Hanwen Li, Zhi Wei +2
Large language model (LLM) agents can self-evolve at test time by accumulating experience: retrieving relevant memories before acting and writing new lessons back afterwards. Yet m…
Fully Unleashing the Multimodal Attacker: Meta-Adaptive Jailbreaking of Vision-Language Models
Benlei Cui, Shen Pang, Yuke Wang +7
The safety of large vision-language models is increasingly stress-tested by multimodal jailbreaks, yet existing attacks remain largely static at the meta level: template-based atta…
Patronus: Safeguarding Text-to-Image Models against White-Box Adversaries
Xinfeng Li, Shengyuan Pang, Jialin Wu +5
Text-to-image (T2I) models, though exhibiting remarkable creativity in image generation, can be exploited to produce unsafe images. Existing safety measures, e.g., content moderati…
Protego: Detecting Adversarial Examples for Vision Transformers via Intrinsic Capabilities
Jialin Wu, Kaikai Pan, Yanjiao Chen +3
Transformer models have excelled in natural language tasks, prompting the vision community to explore their implementation in computer vision problems. However, these models are st…
Legilimens: Practical and Unified Content Moderation for Large Language Model Services
Jialin Wu, Jiangyi Deng, Shengyuan Pang +4
Given the societal impact of unsafe content generated by large language models (LLMs), ensuring that LLM services comply with safety standards is a crucial concern for LLM service…
SOPHON: Non-Fine-Tunable Learning to Restrain Task Transferability For Pre-trained Models
Jiangyi Deng, Shengyuan Pang, Yanjiao Chen +4
Instead of building deep learning models from scratch, developers are more and more relying on adapting pre-trained models to their customized tasks. However, powerful pre-trained…
Towards Efficient Deep Hashing Retrieval: Condensing Your Data via Feature-Embedding Matching
Tao Feng, Jie Zhang, Huashan Liu +2
Deep hashing retrieval has gained widespread use in big data retrieval due to its robust feature extraction and efficient hashing process. However, training advanced deep hashing m…