5 citations · 7 across the 16 of their papers we have counts for
16 papers
X-Mark: Saliency-Guided Robust Dataset Ownership Verification for Medical Imaging
Pranav Kulkarni, Junfeng Guo, Heng Huang
High-quality medical imaging datasets are essential for training deep learning models, but their unauthorized use raises serious copyright and ethical concerns. Medical imaging pre…
An Efficient and Adaptive Watermark Detection System with Tile-based Error Correction
Xinrui Zhong, Xinze Feng, Jingwei Zuo +6
Efficient and reliable detection of generated images is critical for the responsible deployment of generative models. Existing approaches primarily focus on improving detection acc…
A Watermark for Auto-Regressive Image Generation Models
Yihan Wu, Xuehao Cui, Ruibo Chen +2
The rapid evolution of image generation models has revolutionized visual content creation, enabling the synthesis of highly realistic and contextually accurate images for diverse a…
Web Intellectual Property at Risk: Preventing Unauthorized Real-Time Retrieval by Large Language Models
Yisheng Zhong, Yizhu Wen, Junfeng Guo +4
The protection of cyber Intellectual Property (IP) such as web content is an increasingly critical concern. The rise of large language models (LLMs) with online retrieval capabilit…
Modality-Balancing Preference Optimization of Large Multimodal Models by Adversarial Negative Mining
Chenxi Liu, Tianyi Xiong, Yanshuo Chen +5
The task adaptation and alignment of Large Multimodal Models (LMMs) have been significantly advanced by instruction tuning and further strengthened by recent preference optimizatio…
CoTGuard: Using Chain-of-Thought Triggering for Copyright Protection in Multi-Agent LLM Systems
Yan Wen, Junfeng Guo, Heng Huang
As large language models (LLMs) evolve into autonomous agents capable of collaborative reasoning and task execution, multi-agent LLM systems have emerged as a powerful paradigm for…
Asymmetric Conflict and Synergy in Post-training for LLM-based Multilingual Machine Translation
Tong Zheng, Yan Wen, Huiwen Bao +2
The emergence of Large Language Models (LLMs) has advanced the multilingual machine translation (MMT), yet the Curse of Multilinguality (CoM) remains a major challenge. Existing wo…
Improved Unbiased Watermark for Large Language Models
Ruibo Chen, Yihan Wu, Junfeng Guo +1
As artificial intelligence surpasses human capabilities in text generation, the necessity to authenticate the origins of AI-generated content has become paramount. Unbiased waterma…
Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning
Junfeng Guo, Yiming Li, Ruibo Chen +4
Large language models (LLMs) are increasingly integrated into real-world personalized applications through retrieval-augmented generation (RAG) mechanisms to supplement their respo…
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…
A Watermark for Order-Agnostic Language Models
Ruibo Chen, Yihan Wu, Yanshuo Chen +3
Statistical watermarking techniques are well-established for sequentially decoded language models (LMs). However, these techniques cannot be directly applied to order-agnostic LMs,…
De-mark: Watermark Removal in Large Language Models
Ruibo Chen, Yihan Wu, Junfeng Guo +1
Watermarking techniques offer a promising way to identify machine-generated content via embedding covert information into the contents generated from language models (LMs). However…
Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt
Chenxi Liu, Zhenyi Wang, Tianyi Xiong +4
Few-Shot Class-Incremental Learning (FSCIL) models aim to incrementally learn new classes with scarce samples while preserving knowledge of old ones. Existing FSCIL methods usually…
Your Vision-Language Model Itself Is a Strong Filter: Towards High-Quality Instruction Tuning with Data Selection
Ruibo Chen, Yihan Wu, Lichang Chen +6
Data selection in instruction tuning emerges as a pivotal process for acquiring high-quality data and training instruction-following large language models (LLMs), but it is still a…
Federated Continual Novel Class Learning
Lixu Wang, Chenxi Liu, Junfeng Guo +4
In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine learning technique. However, most existing FL studies assume that the data distribution remains…
Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand
Junfeng Guo, Yiming Li, Lixu Wang +4
The prosperity of deep neural networks (DNNs) is largely benefited from open-source datasets, based on which users can evaluate and improve their methods. In this paper, we revisit…