WMVLM: Evaluating Diffusion Model Image Watermarking via Vision-Language Models

2026-01-29

AI Summary

This work addresses the limitations of existing image watermarking evaluation methods, which suffer from the absence of a unified framework, weak interpretability, incomplete security metrics, and inappropriate evaluation criteria for semantic watermarks. To this end, we propose WMVLM—the first unified evaluation framework for diffusion model watermarks based on vision-language models (VLMs). WMVLM employs a three-stage progressive training strategy to jointly perform watermark classification, quality scoring, and interpretable text generation. It is the first framework to unify the evaluation of both residual and semantic watermarks, introducing an interpretability mechanism and redefining quality and security metrics tailored to each type: artifact intensity and erasure resistance for residual watermarks, and latent distribution shift for semantic watermarks. Experiments demonstrate that WMVLM consistently outperforms state-of-the-art VLMs in generalization and evaluation performance across diverse datasets, diffusion models, and watermarking methods.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Digital watermarking is essential for securing generated images from diffusion models. Accurate watermark evaluation is critical for algorithm development, yet existing methods have significant limitations: they lack a unified framework for both residual and semantic watermarks, provide results without interpretability, neglect comprehensive security considerations, and often use inappropriate metrics for semantic watermarks. To address these gaps, we propose WMVLM, the first unified and interpretable evaluation framework for diffusion model image watermarking via vision-language models (VLMs). We redefine quality and security metrics for each watermark type: residual watermarks are evaluated by artifact strength and erasure resistance, while semantic watermarks are assessed through latent distribution shifts. Moreover, we introduce a three-stage training strategy to progressively enable the model to achieve classification, scoring, and interpretable text generation. Experiments show WMVLM outperforms state-of-the-art VLMs with strong generalization across datasets, diffusion models, and watermarking methods.

Problem

• digital watermarking
• diffusion models
• vision-language models
• semantic watermarks
• evaluation framework

Innovation

• vision-language models
• diffusion model watermarking
• unified evaluation framework
• interpretable assessment
• semantic watermark