  Invisible image watermarking is essential for image copyright protection.
Compared to RGB images, RAW format images use a higher dynamic range to capture
the radiometric characteristics of the camera sensor, providing greater
flexibility in post-processing and retouching. Similar to the master recording
in the music industry, RAW images are considered the original format for
distribution and image production, thus requiring copyright protection.
Existing watermarking methods typically target RGB images, leaving a gap for
RAW images. To address this issue, we propose the first deep learning-based RAW
Image Watermarking (RAWIW) framework for copyright protection. Unlike RGB image
watermarking, our method achieves cross-domain copyright protection. We
directly embed copyright information into RAW images, which can be later
extracted from the corresponding RGB images generated by different
post-processing methods. To achieve end-to-end training of the framework, we
integrate a neural network that simulates the ISP pipeline to handle the
RAW-to-RGB conversion process. To further validate the generalization of our
framework to traditional ISP pipelines and its robustness to transmission
distortion, we adopt a distortion network. This network simulates various types
of noises introduced during the traditional ISP pipeline and transmission.
Furthermore, we employ a three-stage training strategy to strike a balance
between robustness and concealment of watermarking. Our extensive experiments
demonstrate that RAWIW successfully achieves cross-domain copyright protection
for RAW images while maintaining their visual quality and robustness to ISP
pipeline distortions.
