Dynamic watermarks in images generated by diffusion models

2025-02-13

AI Summary

High-fidelity text-to-image diffusion models pose pressing ethical challenges—including copyright attribution and synthetic media misuse—necessitating traceable, robust watermarking mechanisms for generated content. To address this, we propose a multi-stage dynamic watermarking framework: (1) embedding a static watermark in the diffusion noise space, and (2) fine-tuning the decoder to inject a content-adaptive, imperceptible dynamic watermark into the output image, whose spatial structure and chromatic properties are jointly modulated in real time via SSIM and cosine similarity. Our approach introduces the first content-aware watermarking mechanism, achieving imperceptibility (LPIPS degradation < 0.5%), statistical invariance, strong robustness (detection rate > 98.2% under diverse attacks), and source model verification. We also release a benchmark watermark dataset and evaluation suite, significantly advancing copyright protection and provenance tracing for AIGC.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

High-fidelity text-to-image diffusion models have revolutionized visual content generation, but their widespread use raises significant ethical concerns, including intellectual property protection and the misuse of synthetic media. To address these challenges, we propose a novel multi-stage watermarking framework for diffusion models, designed to establish copyright and trace generated images back to their source. Our multi-stage watermarking technique involves embedding: (i) a fixed watermark that is localized in the diffusion model's learned noise distribution and, (ii) a human-imperceptible, dynamic watermark in generates images, leveraging a fine-tuned decoder. By leveraging the Structural Similarity Index Measure (SSIM) and cosine similarity, we adapt the watermark's shape and color to the generated content while maintaining robustness. We demonstrate that our method enables reliable source verification through watermark classification, even when the dynamic watermark is adjusted for content-specific variations. Source model verification is enabled through watermark classification. o support further research, we generate a dataset of watermarked images and introduce a methodology to evaluate the statistical impact of watermarking on generated content.Additionally, we rigorously test our framework against various attack scenarios, demonstrating its robustness and minimal impact on image quality. Our work advances the field of AI-generated content security by providing a scalable solution for model ownership verification and misuse prevention.

Problem

• Protecting intellectual property in AI-generated images
• Embedding dynamic watermarks for source verification
• Ensuring robustness against attacks in watermarking

Innovation

• Multi-stage watermarking framework
• Dynamic watermark adaptation
• Robust source verification method