Research article Special Issues

A reversible database watermarking method with low distortion

  • Received: 18 January 2019 Accepted: 09 April 2019 Published: 07 May 2019
  • In this paper, a low distortion reversible database watermarking method based on histogram gap is proposed in view of the large gap in histogram of database integer data. By using the method, the tolerance of the attribute column containing all integer data is firstly calculated and the prediction error is obtained according to the tolerance. Then according to the watermark bits to be embedded, the database tuples will be randomly grouped and the histogram can be constructed by using the prediction error. Finally, the histogram correction rule is used to find the histogram peak bin, the number of consecutive non-zero prediction errors on the left and right sides of the peak is obtained, and the histogram shift is performed on the side with a smaller number of non-zero prediction errors, and then the watermark embedding will be realized. The results of the experiments based on the published dataset of FCTD (Forest Cover Type Dataset) show that compared with the existing GAHSW which also considers distortion, the proposed method significantly reduces the number of histogram column shift while embedding the watermarks, greatly reduces the changes to the carrier data, and effectively reduces the database's data distortion caused by watermark embedding.

    Citation: Yan Li, Junwei Wang, Shuangkui Ge, Xiangyang Luo, Bo Wang. A reversible database watermarking method with low distortion[J]. Mathematical Biosciences and Engineering, 2019, 16(5): 4053-4068. doi: 10.3934/mbe.2019200

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  • In this paper, a low distortion reversible database watermarking method based on histogram gap is proposed in view of the large gap in histogram of database integer data. By using the method, the tolerance of the attribute column containing all integer data is firstly calculated and the prediction error is obtained according to the tolerance. Then according to the watermark bits to be embedded, the database tuples will be randomly grouped and the histogram can be constructed by using the prediction error. Finally, the histogram correction rule is used to find the histogram peak bin, the number of consecutive non-zero prediction errors on the left and right sides of the peak is obtained, and the histogram shift is performed on the side with a smaller number of non-zero prediction errors, and then the watermark embedding will be realized. The results of the experiments based on the published dataset of FCTD (Forest Cover Type Dataset) show that compared with the existing GAHSW which also considers distortion, the proposed method significantly reduces the number of histogram column shift while embedding the watermarks, greatly reduces the changes to the carrier data, and effectively reduces the database's data distortion caused by watermark embedding.


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