Zilan Wang — accepted AI-conference papers

2025

SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Models

Zilan Wang, Junfeng Guo, Jiacheng Zhu, Yiming Li, Heng Huang, Muhao Chen +1

CVPR 2025poster

Recent advances in large-scale text-to-image (T2I) diffusion models have enabled a variety of downstream applications. As T2I models require extensive resources for training, they constitute highly valued intellectual property (IP) for their legitimate owners, yet making them incentive targets for u…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2024

MACE: Mass Concept Erasure in Diffusion Models

Shilin Lu, Zilan Wang, Leyang Li, Yanzhu Liu, Adams Wai-Kin Kong

CVPR 2024poster

The rapid expansion of large-scale text-to-image diffusion models has raised growing concerns regarding their potential misuse in creating harmful or misleading content. In this paper we introduce MACE a finetuning framework for the task of mass concept erasure. This task aims to prevent models from…

Cited by 75 Source ↗PDF ↗code ↗Scholar ↗