Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion Models

2025-03-14

AI Summary

Addressing the challenges of traceable image copyright and heterogeneous, largely unidentifiable training data sources in diffusion model training, this paper first reveals that generated images faithfully inherit statistical characteristics of training data in the frequency domain. Leveraging this insight, we propose CoprGuard—a lightweight, robust frequency-domain watermarking framework. It enables reliable provenance tracing with merely 1% watermarked images in the training set, without modifying model architecture or training procedures. Our key contributions include: (i) the first theoretical characterization of spectral feature inheritance in diffusion models; (ii) a low-overhead (1% watermark ratio), high-robustness frequency-domain watermarking scheme applicable to both pretraining and fine-tuning stages; and (iii) end-to-end spectral modeling, robust watermark embedding, and inverse-feature verification, achieving >98% detection accuracy on mainstream models—including SDXL and Stable Diffusion—under severe attacks such as cropping, compression, noise addition, and model fine-tuning.

Technology Category

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

Application Category

• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Diffusion models have achieved remarkable success in novel view synthesis, but their reliance on large, diverse, and often untraceable Web datasets has raised pressing concerns about image copyright protection. Current methods fall short in reliably identifying unauthorized image use, as they struggle to generalize across varied generation tasks and fail when the training dataset includes images from multiple sources with few identifiable (watermarked or poisoned) samples. In this paper, we present novel evidence that diffusion-generated images faithfully preserve the statistical properties of their training data, particularly reflected in their spectral features. Leveraging this insight, we introduce emph{CoprGuard}, a robust frequency domain watermarking framework to safeguard against unauthorized image usage in diffusion model training and fine-tuning. CoprGuard demonstrates remarkable effectiveness against a wide range of models, from naive diffusion models to sophisticated text-to-image models, and is robust even when watermarked images comprise a mere 1% of the training dataset. This robust and versatile approach empowers content owners to protect their intellectual property in the era of AI-driven image generation.

Problem

• Addressing image copyright protection against diffusion models
• Identifying unauthorized image use in diverse generation tasks
• Developing robust watermarking for AI-driven image generation

Innovation

• Frequency domain watermarking for image protection
• CoprGuard framework against unauthorized image usage
• Robust even with minimal watermarked training data