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Watermarking inserts invisible data into content to protect copyright. Theembedded information provides proof of authorship and facilitates trackingillegal distribution, etc. Current robust watermarking techniques have beenproposed to preserve inserted copyright information from various attacks, suchas content modification and watermark removal attack. However, since thewatermark is inserted in the form of noise, there is an inevitable effect ofreducing content visual quality. In general, more robust watermarkingtechniques tend to have larger effect on the quality, and content creators andusers are often reluctant to insert watermarks. Thus, there is a demand for awatermark that maintains maximum image quality, even if the watermarkperformance is slightly inferior. Therefore, we propose a watermarkingtechnique that maximizes invisibility while maintaining sufficient robustnessand data capacity enough to be applied for real situations. The proposed methodminimizes watermarking energy by adopting curvelet domain multi-directionaldecomposition to maximize invisibility, and maximizes robustness against signalprocessing attack by watermarking pattern suitable for curvelet transformation.The method is also robust against geometric attack by employing watermarkdetection method utilizing curvelet characteristics. The proposed method showedvery good results of 57.65 dB peak signal-to-noise ratio in fidelity tests, andmean opinion score showed that images treated with the proposed method werehardly distinguishable from the originals. The proposed technique also showedgood robustness against signal processing and geometric attacks compared withexisting techniques.

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