RAWIW: RAW Image Watermarking Robust to ISP Pipeline
Kang Fu, Xiaohong Liu, Jun Jia, Zicheng Zhang, Yicong Peng, Jia Wang,, and Guangtao Zhai

TL;DR
This paper introduces RAWIW, a deep learning-based RAW image watermarking framework that embeds copyright information directly into RAW images, ensuring robustness across different post-processing pipelines and transmission distortions.
Contribution
It is the first to propose a deep learning approach for RAW image watermarking that works across domains and simulates ISP pipelines for improved robustness.
Findings
Successfully embeds watermarks into RAW images with high invisibility.
Achieves robustness against ISP pipeline variations and transmission noise.
Maintains high visual quality of watermarked images.
Abstract
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…
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Taxonomy
TopicsAdvanced Steganography and Watermarking Techniques · Digital Media Forensic Detection · Generative Adversarial Networks and Image Synthesis
