Zero-watermarking Algorithm for Medical Volume Data Based on Difference Hashing

  • Baoru Han College of Information Science and Technology Hainan University Haikou, 570228, China
  • Jingbing Li College of Information Science and Technology Hainan University Haikou, 570228, China
  • Yujia Li College of Information Science and Technology Hainan University Haikou, 570228, China

Abstract

In order to protect the copyright of medical volume data, a new zerowatermarking algorithm for medical volume data is presented based on Legendre chaotic neural network and difference hashing in three-dimensional discrete cosine transform domain. It organically combines the Legendre chaotic neural network, three-dimensional discrete cosine transform and difference hashing, and becomes a kind of robust zero-watermarking algorithm. Firstly, a new kind of Legendre chaotic neural network is used to generate chaotic sequences, which causes the original watermarking image scrambling. Secondly, it uses three-dimensional discrete cosine transform to the original medical volume data, and the perception of the low frequency coefficient invariance in the three-dimensional discrete cosine transform domain is utilized to extract the first 4*5*4 coefficient in order to form characteristic matrix (16*5). Then, the difference hashing algorithm is used to extract a robust perceptual hashing value which is a binary sequence, with the length being 64-bit. Finally, the hashing value serves as the image features to construct the robust zero-watermarking. The results show that the algorithm can resist the attack, with good robustness and high security.

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Published
2015-04-01
How to Cite
HAN, Baoru; LI, Jingbing; LI, Yujia. Zero-watermarking Algorithm for Medical Volume Data Based on Difference Hashing. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 10, n. 2, p. 188-199, apr. 2015. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/1752>. Date accessed: 22 may 2022.

Keywords

zero-watermarking, medical volume data, difference hashing, Legendre chaotic neural network, three-dimensional discrete cosine transform