MarkDiffusion: An Open-Source Toolkit for Generative Watermarking of Latent Diffusion Models

TL;DR

MarkDiffusion introduces an open-source toolkit for generative watermarking of Latent Diffusion Models, addressing fragmentation and complexity by delivering a unified, modular framework, an interactive mechanism-visualization suite, and a comprehensive evaluation module. It integrates eight state-of-the-art watermarking algorithms, supports image and video generation, and provides 24 evaluation tools plus 8 automated pipelines to benchmark detectability, robustness, and output quality. The approach emphasizes in-process watermarking within the diffusion pipeline, facilitating reproducible experiments and broader accessibility through Diffusers compatibility and user-friendly APIs. Collectively, the work enables researchers to compare watermarking strategies, visualize embedding and detection processes, and assess performance across realistic media scenarios, with potential impact on provenance, IP protection, and public understanding of generative media.

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.

Figures (13)

• Figure 1: Architecture overview of MarkDiffusion.
• Figure 2: Visualization examples using MarkDiffusion.
• Figure 3: Detailed architecture of the unified implementation framework.
• Figure 4: Detailed architecture of the mechanism visualization module.
• Figure 5: Detailed Structure of the evaluation module.