Robust curvelet domain watermarking technique that preserves cleanness of high quality images

TL;DR

This paper introduces a curvelet domain watermarking method that maximizes image quality and robustness, effectively resisting various attacks while maintaining high visual fidelity.

Contribution

It proposes a novel curvelet-based watermarking technique that balances invisibility and robustness, outperforming existing methods in preserving image quality and resisting attacks.

Findings

Achieved 57.65 dB PSNR in fidelity tests.

Images remained visually indistinguishable from originals.

Demonstrated robustness against signal processing and geometric attacks.

Abstract

Watermarking inserts invisible data into content to protect copyright. The embedded information provides proof of authorship and facilitates tracking 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 attack. However, since the watermark is inserted in the form of noise, there is an inevitable effect of reducing content visual quality. In general, more robust watermarking techniques tend to have larger effect on the 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 enough to be applied for real situations. The proposed method minimizes watermarking energy by adopting curvelet domain multi-directional decomposition to maximize invisibility, and maximizes robustness against signal processing attack by watermarking pattern suitable for curvelet transformation. The method is also robust against geometric attack by employing watermark detection method utilizing curvelet characteristics. The proposed method showed very good results of 57.65 dB peak signal-to-noise ratio in fidelity tests, and 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.