Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion Models
This addresses copyright protection for content owners in AI-driven image generation, offering a robust solution against unauthorized use in diffusion model training.
The paper tackles the problem of image copyright protection against diffusion models by showing that these models preserve statistical properties of training data in spectral features, and introduces CoprGuard, a frequency domain watermarking framework that is effective with only 1% watermarked images in the dataset.
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.