12 papers
SEMA-RAG: A Self-Evolving Multi-Agent Retrieval-Augmented Generation Framework for Medical Reasoning
Yongfeng Huang, Ruiying Chen, James Cheng
Retrieval-Augmented Generation (RAG) is widely employed to mitigate risks such as hallucinations and knowledge obsolescence in medical question answering, yet its predominantly sin…
Black-box Membership Inference Attacks on the Pre-training Data of Image-generation Models
Tao Qi, Huili Wang, Yuanhong Huang +6
The rapid advancement of diffusion-based image generation models has raised serious concerns regarding potential copyright and privacy infringements involving human-created data. M…
EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
Tianyun Zhang, Zhen Yang, Haozhao Wang +2
Federated learning faces increasing threats from model poisoning attacks, which harms its application to improve privacy. Existing defense methods typically rely on fixed threshold…
Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation
Peiru Yang, Haoran Zheng, Tong Ju +6
Retrieval-augmented generation (RAG) is a widely adopted paradigm for enhancing LLMs in medical applications by incorporating expert multimodal knowledge during generation. However…
ADEPT: An Entropy-Driven Dual-Strategy Agent for Interactive Video Retrieval
Ke Chen, Shengyuan Han, Yongfeng Huang +4
This research aims to solve the challenge of video retrieval from massive datasets, caused by ambiguous user queries. Prevailing single-round retrieval paradigms face a performance…
LiveSecBench: A Dynamic and Event-Driven Safety Benchmark for Chinese Language Model Applications
Yudong Li, Peiru Yang, Feng Huang +18
We introduce LiveSecBench, a continuously updated safety benchmark specifically for Chinese-language LLM application scenarios. LiveSecBench constructs a high-quality and unique da…
Black-Box Membership Inference Attack for LVLMs via Prior Knowledge-Calibrated Memory Probing
Jinhua Yin, Peiru Yang, Chen Yang +5
Large vision-language models (LVLMs) derive their capabilities from extensive training on vast corpora of visual and textual data. Empowered by large-scale parameters, these models…
RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Lifestyle Social Media
Yudong Li, Yufei Sun, Peiru Yang +7
We introduce RedNote-Vibe, a dataset spanning five years (pre-LLM to July 2025) sourced from lifestyle platform RedNote (Xiaohongshu), capturing the temporal dynamics of content cr…
Enhancing Watermarking Quality for LLMs via Contextual Generation States Awareness
Peiru Yang, Xintian Li, Wanchun Ni +6
Recent advancements in watermarking techniques have enabled the embedding of secret messages into AI-generated text (AIGT), serving as an important mechanism for AIGT detection. Ex…
MrM: Black-Box Membership Inference Attacks against Multimodal RAG Systems
Peiru Yang, Jinhua Yin, Haoran Zheng +7
Multimodal retrieval-augmented generation (RAG) systems enhance large vision-language models by integrating cross-modal knowledge, enabling their increasing adoption across real-wo…
HeteRAG: A Heterogeneous Retrieval-augmented Generation Framework with Decoupled Knowledge Representations
Peiru Yang, Xintian Li, Zhiyang Hu +8
Retrieval-augmented generation (RAG) methods can enhance the performance of LLMs by incorporating retrieved knowledge chunks into the generation process. In general, the retrieval…
Evidencing Unauthorized Training Data from AI Generated Content using Information Isotopes
Qi Tao, Yin Jinhua, Cai Dongqi +10
In light of scaling laws, many AI institutions are intensifying efforts to construct advanced AIs on extensive collections of high-quality human data. However, in a rush to stay co…