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Reversible Data Hiding in Binary Images by Symmetrical Flipping Degree Histogram Modification

  • Xiaolin Yin
  • Wei LuEmail author
  • Wanteng Liu
  • Junhong Zhang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 895)

Abstract

In recent years, reversible data hiding in binary images has become more and more important in the field of data hiding. For the binary property, hiding information in binary images can only be achieved by flipping pixels. But flipping the pixels randomly in a binary image will cause remarkable distortion. The existing methods are capable of reversible data hiding in binary images but the visual quality is not good. In this paper, we propose a novel reversible data hiding method based on symmetrical flipping degree (SFD) histogram modification. First, we define the SFD of the block, which is a visual score measured the distance between the pixels in the block. The smaller SFD is, the better the visual quality the image has. Then the histogram is generated from the SFD, and it utilizes the zero or the minimum point and modifies the pixels with high SFD for its pre-processing. Finally, the appropriate corresponding pixels are selected adaptively to flip for information embedding. Experimental results demonstrate the feasibility of our proposed method and the visual quality is satisfactory.

Keywords

Reversible data hiding Symmetrical flipping degree (SFD) Histogram modification Binary image 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. U1736118), the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), the Key Project of Scientific Research Plan of Guangzhou (No. 201804020068), Shanghai Minsheng Science and Technology Support Program (17DZ1205500), Shanghai Sailing Program (17YF1420000), the Fundamental Research Funds for the Central Universities (No. 16lgjc83 and No. 17lgjc45).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Data and Computer Science, Guangdong Key Laboratory of Information Security Technology, Ministry of Education Key Laboratory of Machine Intelligence and Advanced ComputingSun Yat-sen UniversityGuangzhouChina

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