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Data hiding based on extended turtle shell matrix construction method

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Abstract

Data hiding research has focused mainly on determining how to embed secret data into various public host media, and to also ensure the host medium is not changed to a degree such that it can be perceived by the human eye. In 2014, Chang et al. proposed a novel concept, named the turtle shell matrix, to embed secret data. This scheme has obvious advantages with respect to its hiding capacity and image quality. However, its disadvantage is lack of flexibility due to the fixed turtle shell matrix structure. In this paper, we extend this turtle shell matrix structure into a different matrix model to meet different hiding capacity and image quality needs. Meanwhile, a general extraction function is derived to generate a matrix having a different turtle shell model. The values of the pixel pairs in the cover image are modified according to guidance provided by the turtle shell to hide a secret digit in an N-ary notational system. The experimental results show that the proposed scheme not only has better flexibility in balancing the trade-off between hiding capacity and stego-image quality, but also provides higher hiding capacity and stego-images with better visual quality than previous schemes.

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Acknowledgements

This work was supported in part by The National Natural Science Foundation of China (No.61540009, No.61272262).

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Authors and Affiliations

  1. School of Computer Science, Northwestern Polytechnical University, Xi’ an, 710072, China

    Li Liu

  2. College of Electronic Information and Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China

    Li Liu & Anhong Wang

  3. Department of Information Engineering and Computer Science, Feng Chia University, Taichung, 40724, Taiwan

    Chin-Chen Chang

Authors
  1. Li Liu
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  2. Chin-Chen Chang
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  3. Anhong Wang
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Correspondence to Chin-Chen Chang.

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Liu, L., Chang, CC. & Wang, A. Data hiding based on extended turtle shell matrix construction method. Multimed Tools Appl 76, 12233–12250 (2017). https://doi.org/10.1007/s11042-016-3624-7

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