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Cognitive Computation of Compressed Sensing for Watermark Signal Measurement

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Abstract

As an important tool for protecting multimedia contents, scrambling and randomizing of original messages is used in generating digital watermark for satisfying security requirements. Based on the neural perception of high-dimensional data, compressed sensing (CS) is proposed as a new technique in watermarking for improved security and reduced computational complexity. In our proposed methodology, watermark signal is extracted from the CS of the Hadamard measurement matrix. Through construction of the scrambled block Hadamard matrix utilizing a cryptographic key, encrypting the watermark signal in CS domain is achieved without any additional computation required. The extensive experiments have shown that the neural inspired CS mechanism can generate watermark signal of higher security, yet it still maintains a better trade-off between transparency and robustness.

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Acknowledgments

The authors would like to thank the editors and the anonymous reviewers for their constructive comments to further improve the quality of this paper. We’d also great thank the support from the National Natural Science Foundation of China under Grant 61272381, Key Reserach and Development Project of Guangdong Province (grant 2014KZDXM060) and Natural Science Foundation of Guangdong, China (grant 2015A030313672).

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Authors and Affiliations

  1. Guangdong Polytechnic Normal University, Guangdong, China

    Huimin Zhao

  2. University of Strathclyde, Glasgow, UK

    Jinchang Ren

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  1. Huimin Zhao
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Correspondence to Jinchang Ren.

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Zhao, H., Ren, J. Cognitive Computation of Compressed Sensing for Watermark Signal Measurement. Cogn Comput 8, 246–260 (2016). https://doi.org/10.1007/s12559-015-9357-5

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  • DOI: https://doi.org/10.1007/s12559-015-9357-5

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