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Reversible data hiding techniques with high message embedding capacity in images

Author

Listed:
  • Furqan Aziz
  • Taeeb Ahmad
  • Abdul Haseeb Malik
  • M Irfan Uddin
  • Shafiq Ahmad
  • Mohamed Sharaf

Abstract

Reversible Data Hiding (RDH) techniques have gained popularity over the last two decades, where data is embedded in an image in such a way that the original image can be restored. Earlier works on RDH was based on the Image Histogram Modification that uses the peak point to embed data in the image. More recent works focus on the Difference Image Histogram Modification that exploits the fact that the neighbouring pixels of an image are highly correlated and therefore the difference of image makes more space to embed large amount of data. In this paper we propose a framework to increase the embedding capacity of reversible data hiding techniques that use a difference of image to embed data. The main idea is that, instead of taking the difference of the neighboring pixels, we rearrange the columns (or rows) of the image in a way that enhances the smooth regions of an image. Any difference based technique to embed data can then be used in the transformed image. The proposed method is applied on different types of images including textures, patterns and publicly available images. Experimental results demonstrate that the proposed method not only increases the message embedding capacity of a given image by more than 50% but also the visual quality of the marked image containing the message is more than the visual quality obtained by existing state-of-the-art reversible data hiding technique. The proposed technique is also verified by Pixel Difference Histogram (PDH) Stegoanalysis and results demonstrate that marked images generated by proposed method is undetectable by PDH analysis.

Suggested Citation

  • Furqan Aziz & Taeeb Ahmad & Abdul Haseeb Malik & M Irfan Uddin & Shafiq Ahmad & Mohamed Sharaf, 2020. "Reversible data hiding techniques with high message embedding capacity in images," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-24, May.
  • Handle: RePEc:plo:pone00:0231602
    DOI: 10.1371/journal.pone.0231602
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    References listed on IDEAS

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    1. Sayantan Mitra & Sriparna Saha, 2019. "A multiobjective multi-view cluster ensemble technique: Application in patient subclassification," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-30, May.
    2. Jianfa Wu & Dahao Peng & Zhuping Li & Li Zhao & Huanzhang Ling, 2015. "Network Intrusion Detection Based on a General Regression Neural Network Optimized by an Improved Artificial Immune Algorithm," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-13, March.
    3. Syed Atif Ali Shah & Irfan Uddin & Furqan Aziz & Shafiq Ahmad & Mahmoud Ahmad Al-Khasawneh & Mohamed Sharaf, 2020. "An Enhanced Deep Neural Network for Predicting Workplace Absenteeism," Complexity, Hindawi, vol. 2020, pages 1-12, February.
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