6 citations · 8 across the 15 of their papers we have counts for
8 papers · 1 filter
A Vision-Language Pre-training Model-Guided Approach for Mitigating Backdoor Attacks in Federated Learning
Keke Gai, Dongjue Wang, Jing Yu +2
Defending backdoor attacks in Federated Learning (FL) under heterogeneous client data distributions encounters limitations balancing effectiveness and privacy-preserving, while mos…
AGATE: Stealthy Black-box Watermarking for Multimodal Model Copyright Protection
Jianbo Gao, Keke Gai, Jing Yu +2
Recent advancement in large-scale Artificial Intelligence (AI) models offering multimodal services have become foundational in AI systems, making them prime targets for model theft…
PCDiff: Proactive Control for Ownership Protection in Diffusion Models with Watermark Compatibility
Keke Gai, Ziyue Shen, Jing Yu +2
With the growing demand for protecting the intellectual property (IP) of text-to-image diffusion models, we propose PCDiff -- a proactive access control framework that redefines mo…
Missing Target-Relevant Information Prediction with World Model for Accurate Zero-Shot Composed Image Retrieval
Yuanmin Tang, Jing Yu, Keke Gai +4
Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent across domain, scene, object, and attribute. The key cha…
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification
Xiangyan Qu, Jing Yu, Jiamin Zhuang +3
Zero-shot learning (ZSL) aims to train a model on seen classes and recognize unseen classes by knowledge transfer through shared auxiliary information. Recent studies reveal that d…
Secure and Efficient Watermarking for Latent Diffusion Models in Model Distribution Scenarios
Liangqi Lei, Keke Gai, Jing Yu +2
Latent diffusion models have exhibited considerable potential in generative tasks. Watermarking is considered to be an alternative to safeguard the copyright of generative models a…
Vertical Federated Continual Learning via Evolving Prototype Knowledge
Shuo Wang, Keke Gai, Jing Yu +2
Vertical Federated Learning (VFL) has garnered significant attention as a privacy-preserving machine learning framework for sample-aligned feature federation. However, traditional…
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks
Keke Gai, Mohan Wang, Jing Yu +2
Multimodal Federated Learning (MFL) with mixed modalities enables unimodal and multimodal clients to collaboratively train models while ensuring clients' privacy. As a representati…