Halftone Image Steganography with Flippability Measurement Based on Pixel Pairs

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


In recent years, many state-of-the-art steganographic schemes for halftone images have been proposed. Most of them only focus on the visual quality of the marked image but ignore the statistical security. In this paper, a novel steganographic scheme for halftone images is proposed, which focuses on both visual imperceptibility and statistical security. For halftone steganography, embedding messages can only be achieved by flipping pixels. The significant distortion will occur if the pixels are flipped arbitrarily in a halftone image. To select the optimal flipping pixels, we define the flippability measurement based on pixel pairs, which can provide a flippability score for each pixel in halftone images. The higher flippability score of a pixel means that the visual distortion caused by flipping the pixel will be smaller, so the pixels with high flippability scores can be selected as carriers. To achieve high embedding capacity and minimize the embedding distortions, syndrome-trellis code (STC) is employed in the embedding process. The experimental results demonstrate that the proposed scheme can achieve high embedding capacity and realize acceptable statistical security with high visual imperceptibility.


Halftone image steganography Flippability measurement Pixel pairs Syndrome-trellis code (STC) 



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).


  1. 1.
    Bas, P., Filler, T., Pevn, Y.T.: Break our steganographic system: the ins and outs of organizing BOSS. J. Am. Stat. Assoc. 96(454), 488–499 (2011)Google Scholar
  2. 2.
    Chen, J., Lu, W., Fang, Y., Liu, X., Yeung, Y., Xue, Y.: Binary image steganalysis based on local texture pattern. J. Vis. Commun. Image Represent. 55, 149–156 (2018)CrossRefGoogle Scholar
  3. 3.
    Feng, B., Lu, W., Sun, W.: Secure binary image steganography based on minimizing the distortion on the texture. IEEE Trans. Inf. Forensics Secur. 10(2), 243–255 (2014)CrossRefGoogle Scholar
  4. 4.
    Feng, B., Lu, W., Sun, W.: Binary image steganalysis based on pixel mesh markov transition matrix. J. Vis. Commun. Image Represent. 26(C), 284–295 (2015)CrossRefGoogle Scholar
  5. 5.
    Filler, T., Judas, J., Fridrich, J.: Minimizing additive distortion in steganography using syndrome-trellis codes. IEEE Trans. Inf. Forensics Secur. 6(3), 920–935 (2011)CrossRefGoogle Scholar
  6. 6.
    Floyd, R.W.: An adaptive algorithm for spatial grayscale. In: Proceedings of SID International Symposium Digest of Technical Papers, pp. 75–77 (1975)Google Scholar
  7. 7.
    Floyd, R.W., Steinberg, L.: Adaptive algorithm for spatial greyscale. In: Proceedings of SID, pp. 75–77 (1976)Google Scholar
  8. 8.
    Fu, M.S., Au, O.C.: Data hiding watermarking for halftone images. IEEE Trans. Image Process. 11(4), 477–84 (2002)CrossRefGoogle Scholar
  9. 9.
    Fu, M.S., Au, O.C.: Halftone image data hiding with intensity selection and connection selection. Signal Process. Image Commun. 16(10), 909–930 (2001)CrossRefGoogle Scholar
  10. 10.
    Guo, J.M., Liu, Y.F.: Halftone-image security improving using overall minimal-error searching. IEEE Trans. Image Process. 20, 2800–2812 (2011)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Guo, M., Zhang, H.: High capacity data hiding for halftone image authentication. In: International Conference on Digital Forensics and Watermarking, pp. 156–168 (2013)Google Scholar
  12. 12.
    Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41(6), 765–781 (2011)CrossRefGoogle Scholar
  13. 13.
    Li, J., Lu, W., Weng, J., Mao, Y., Li, G.: Double JPEG compression detection based on block statistics. Multimed. Tools Appl. 2, 1–16 (2018)Google Scholar
  14. 14.
    Lien, B.K., Lan, Z.L.: Improved halftone data hiding scheme using hilbert curve neighborhood toggling. In: Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 73–76 (2011)Google Scholar
  15. 15.
    Lien, B.K., Pei, W.D.: Reversible data hiding for ordered dithered halftone images. In: IEEE International Conference on Image Processing, pp. 4181–4184 (2009)Google Scholar
  16. 16.
    Liu, X., Lu, W., Huang, T., Liu, H., Xue, Y., Yeung, Y.: Scaling factor estimation on jpeg compressed images by cyclostationarity analysis. Multimed. Tools Appl., 1–18 (2018).
  17. 17.
    Lu, H., Kot, A.C., Shi, Y.Q.: Distance-reciprocal distortion measure for binary document images. IEEE Signal Process. Lett. 11(2), 228–231 (2004)CrossRefGoogle Scholar
  18. 18.
    Lu, W., He, L., Yeung, Y., Xue, Y., Liu, H., Feng, B.: Secure binary image steganography based on fused distortion measurement. IEEE Trans. Circuits Syst. Video Technol. PP(99), 1–1 (2018)Google Scholar
  19. 19.
    Mars, J., Au, O.C.: Data hiding by smart pair toggling for halftone images. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2000, vol. 4, pp. 2318–2321 (2000)Google Scholar
  20. 20.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2000)CrossRefGoogle Scholar
  21. 21.
    Pei, S.C., Guo, J.M.: High-capacity data hiding in halftone images using minimal error bit searching. In: International Conference on Image Processing, vol. 5, pp. 3463–3466 (2004)Google Scholar
  22. 22.
    Ulichney, R.: Digital Halftoning. MIT Press, Cambridge (1987)Google Scholar
  23. 23.
    Wang, B., Li, X.F., Liu, F., Hu, F.Q.: Color text image binarization based on binary texture analysis. Pattern Recognit. Lett. 26(11), 1650–1657 (2005)CrossRefGoogle Scholar
  24. 24.
    Xue, Y., Liu, W., Lu, W., Yeung, Y., Liu, X., Liu, H.: Efficient halftone image steganography based on dispersion degree optimization. J. Real-Time Image Proc., 1–9 (2018).
  25. 25.
    Yoo, J.C., Ahn, C.W.: Image matching using peak signal-to-noise ratio-based occlusion detection. IET Image Process. 6(5), 483–495 (2012)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Zhang, Q., Lu, W., Wang, R., Li, G.: Digital image splicing detection based on markov features in block DWT domain. Multimed. Tools Appl. 3, 1–22 (2018)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Data and Computer Science, Guangdong Key Laboratory of Information Security TechnologyMinistry of Education Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen UniversityGuangzhouChina

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