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Invisible image watermarking is essential for image copyright protection.Compared to RGB images, RAW format images use a higher dynamic range to capturethe radiometric characteristics of the camera sensor, providing greaterflexibility in post-processing and retouching. Similar to the master recordingin the music industry, RAW images are considered the original format fordistribution and image production, thus requiring copyright protection.Existing watermarking methods typically target RGB images, leaving a gap forRAW images. To address this issue, we propose the first deep learning-based RAWImage Watermarking (RAWIW) framework for copyright protection. Unlike RGB imagewatermarking, our method achieves cross-domain copyright protection. Wedirectly embed copyright information into RAW images, which can be laterextracted from the corresponding RGB images generated by differentpost-processing methods. To achieve end-to-end training of the framework, weintegrate a neural network that simulates the ISP pipeline to handle theRAW-to-RGB conversion process. To further validate the generalization of ourframework to traditional ISP pipelines and its robustness to transmissiondistortion, we adopt a distortion network. This network simulates various typesof noises introduced during the traditional ISP pipeline and transmission.Furthermore, we employ a three-stage training strategy to strike a balancebetween robustness and concealment of watermarking. Our extensive experimentsdemonstrate that RAWIW successfully achieves cross-domain copyright protectionfor RAW images while maintaining their visual quality and robustness to ISPpipeline distortions.

RAWIW: RAW Image Watermarking Robust to ISP Pipeline