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20242026
most citedSecure and Efficient Watermarking for Latent Diffusion Models in Model Distribution Scenarios

1 citations · 1 across the 5 of their papers we have counts for

collaborators

10 papers

eess.IV2026

ALIEN: Analytic Latent Watermarking for Controllable Generation

Liangqi Lei, Keke Gai, Jing Yu +1

Watermarking is a technical alternative to safeguarding intellectual property and reducing misuse. Existing methods focus on optimizing watermarked latent variables to balance wate…

cs.LG2025

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…

cs.SE2025

LLAMA: Multi-Feedback Smart Contract Fuzzing Framework with LLM-Guided Seed Generation

Keke Gai, Haochen Liang, Jing Yu +2

Smart contracts play a pivotal role in blockchain ecosystems, and fuzzing remains an important approach to securing smart contracts. Even though mutation scheduling is a key factor…

cs.CR2025

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…

cs.CR2025

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…

cs.CR20251 cited

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…