Robust watermarking in curvelet domain for preserving cleanness of high-quality images

TLDR: A watermarking technique that maximizes invisibility while maintaining sufficient robustness and data capacity to be applied in real situations is proposed and showed very good results of a 57.65 dB peak signal-to-noise ratio in fidelity tests, and the mean opinion score showed that images treated with the proposed method were hardly distinguishable from the originals.

Abstract: Watermarking inserts invisible data into content to protect copyright. The embedded information provides proof of authorship and facilitates the tracking of illegal distribution, etc. Current robust watermarking techniques have been proposed to preserve inserted copyright information from various attacks, such as content modification and watermark removal attacks. However, since the watermark is inserted in the form of noise, there is an inevitable effect of reducing content visual quality. In general, most robust watermarking techniques tend to have a greater effect on quality, and content creators and users are often reluctant to insert watermarks. Thus, there is a demand for a watermark that maintains maximum image quality, even if the watermark performance is slightly inferior. Therefore, we propose a watermarking technique that maximizes invisibility while maintaining sufficient robustness and data capacity to be applied in real situations. The proposed method minimizes watermarking energy by adopting curvelet domain multi-directional decomposition to maximize invisibility and maximizes robustness against signal processing attacks with a watermarking pattern suitable for curvelet transformation. The method is also robust against geometric attacks by employing the watermark detection method utilizing curvelet characteristics. The proposed method showed very good results of a 57.65 dB peak signal-to-noise ratio in fidelity tests, and the mean opinion score showed that images treated with the proposed method were hardly distinguishable from the originals. The proposed technique also showed good robustness against signal processing and geometric attacks compared with existing techniques.