Joint robustness and security enhancement for feature-based image watermarking using invariant feature regions

Jen Sheng Tsai, Win Bin Huang, Yau Hwang Kuo, Mong Fong Horng

研究成果: Article

43 引文 斯高帕斯(Scopus)

摘要

Local image features have been widely applied in feature-based watermarking schemes. The feature invariance is exploited to achieve robustness against attacks, but the leakage of information about hidden watermarks from publicly known locations and sizes of features are often unconsidered in security. This paper, therefore, proposes a novel image watermarking approach, which adopts invariant feature regions to jointly enhance its robustness and security. Initially, circular feature regions are determined by the scale-adapted auto-correlation matrix and the Laplacian-of-Gaussian operation. Leakage of secret information is also controlled carefully during feature detection procedure. An optimal selection process formulated as a multidimensional knapsack problem is then proposed to select robust non-overlapping regions from those circular feature regions to resist various attacks. This process is implemented by a genetic algorithm-based approach, and incorporates randomization to mitigate the security risk. Finally, each selected region is normalized to obtain a geometrically invariant feature region, and embedded with a region-dependent watermark to overcome the weakness of multiple-redundant watermarks. The evaluation results based on the StirMark benchmark present the proposed scheme can tolerate various attacks, including noise-like signal processing and geometric distortions. A security analysis in terms of differential entropy also confirms the security improvement of the proposed method.

原文English
頁(從 - 到)1431-1445
頁數15
期刊Signal Processing
92
發行號6
DOIs
出版狀態Published - 2012 六月 1

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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