FPW: Frequency-Domain Pixel-by-Pixel Watermarking Against Unauthorized Images Used on Training Generative Model

The proliferation of diffusion-based generative models has significantly advanced the field of synthetic image generation. However, the lack of transparency in training datasets, often not open-sourced, raises substantial concerns about copyright infringement, potentially involving the unauthorized use of proprietary images. To address this issue, we introduce Forensic Provenance Watermarking (FPW), a novel watermarking approach designed specifically for diffusion models. Specifically, FPW embeds a detectable pattern into the training data that, when used without permission, manifests in the generated images. This unique pattern serves as forensic evidence of data misuse, enabling data owners to legally challenge entities that misuse their copyrighted material. Our method simplifies watermark designs and utilizes spatial relationships within the images, employing a surrogate generative model to ensure robustness across various conditions. Experimental results on multiple datasets manifest that FPW maintains high detection rates even at low poisoning ratios, making it an effective tool for copyright protection in the era of advanced generative models. The code is available on the project page.