6 papers
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…
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…
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…
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…