RAIN: Region-Aware Inversion Network for Semantic Watermark Extraction
| Source: arXiv AI
Tags: diffusion models, watermarking, AI-generated content, Gaussian Shading, content authentication, image generation
RAIN proposes a one-step, prompt-free watermark extractor for diffusion models that decomposes endpoint recovery into an image anchor and noise residual—avoiding the multi-step inversion typically required for Gaussian Shading extraction, with lower computational cost than OSI and FARI methods.
Details
Semantic watermarks for diffusion models embed ownership information into the generative process, but extracting them with Gaussian Shading typically requires expensive multi-step diffusion inversion. Recent one-step methods reduce this cost substantially, and RAIN advances the approach further using flow matching theory.\n\nThe key observation: near the high-SNR image endpoint, recovering a useful noise statistic from the first step of extended flow matching is much simpler than reconstructing the full inverse trajectory, and Gaussian Shading only needs the recovered latent to remain in the correct watermark decision region. RAIN decomposes endpoint recovery into an image-like anchor plus a noise-oriented residual—enabling efficient GPU parallel computation.\n\nThe resulting extractor is one-step, prompt-free, and avoids iterative evaluation of a diffusion-scale U-Net. Computational cost is lower than both OSI and FARI. A GitHub repository is linked. For teams deploying AI image generation and needing efficient watermark verification at scale, this reduces verification overhead significantly.