{"ID":2872098,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10569","arxiv_id":"2509.10569","title":"MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models","abstract":"We introduce MarkDiffusion, an open-source Python toolkit for generative watermarking of latent diffusion models. It comprises three key components: a unified implementation framework for streamlined watermarking algorithm integrations and user-friendly interfaces; a mechanism visualization suite that intuitively showcases added and extracted watermark patterns to aid public understanding; and a comprehensive evaluation module offering standard implementations of 24 tools across three essential aspects - detectability, robustness, and output quality - plus 8 automated evaluation pipelines. Through MarkDiffusion, we seek to assist researchers, enhance public awareness and engagement in generative watermarking, and promote consensus while advancing research and applications.","short_abstract":"We introduce MarkDiffusion, an open-source Python toolkit for generative watermarking of latent diffusion models. It comprises three key components: a unified implementation framework for streamlined watermarking algorithm integrations and user-friendly interfaces; a mechanism visualization suite that intuitively showc...","url_abs":"https://arxiv.org/abs/2509.10569","url_pdf":"https://arxiv.org/pdf/2509.10569v2","authors":"[\"Leyi Pan\",\"Sheng Guan\",\"Zheyu Fu\",\"Luyang Si\",\"Huan Wang\",\"Zian Wang\",\"Hanqian Li\",\"Xuming Hu\",\"Irwin King\",\"Philip S. Yu\",\"Aiwei Liu\",\"Lijie Wen\"]","published":"2025-09-11T07:57:22Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.AI\",\"cs.MM\"]","methods":"[\"Diffusion Model\"]","has_code":false}