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Research on database watermarking based on Independent Component Analysis and multiple rolling

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Listed:
  • Zaihui Cao
  • Ge Shi
  • Qingtao Wu

Abstract

Digital watermarking is an important branch of information hiding technology research field. It is a technology that embeds identifiable data into digital works. In order to protect the copyright of digital products, the basic idea is to embed confidential information in digital products such as images, audio, and video to realize data fusion. The current digital watermarking technology is mainly concentrated in the field of multimedia information (such as images, audio, and video) and made good progress. In this article, based on the large database capacity, and much numerical data characteristics, we proposed a separate component analysis on the database watermarking method. First, this article uses a one-way hash function as the marking algorithm to determine the tuple according to the user’s given key, the tuple primary key value, and the tuple scale that needs to be marked. Then the watermark is extracted through the key, and the watermarked information is taken out in the database. The matrix is separated by the ratio Independent Component Analysis algorithm, and the watermark is used to separate the matrix. This kind of watermark information is rolled up, and the information in the original database is kept independent of each other, and the embedded watermark information is changed by the smaller carrier. The Independent Component Analysis method is used to extract the watermark image, and the ratio Independent Component Analysis method is used to solve the problem of the influence of the uncertainty of the arrangement order. The experimental results show that the proposed method has a good detection effect.

Suggested Citation

  • Zaihui Cao & Ge Shi & Qingtao Wu, 2019. "Research on database watermarking based on Independent Component Analysis and multiple rolling," International Journal of Distributed Sensor Networks, , vol. 15(4), pages 15501477198, April.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:4:p:1550147719841004
    DOI: 10.1177/1550147719841004
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    References listed on IDEAS

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    1. Ajay Kumar & Ravi Shankar & Alok Choudhary & Lakshman S. Thakur, 2016. "A big data MapReduce framework for fault diagnosis in cloud-based manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7060-7073, December.
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