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Halftone Image Steganography with Distortion Measurement Based on Structural Similarity

  • Wanteng Liu
  • Xiaolin Yin
  • Wei LuEmail author
  • Junhong Zhang
Conference paper
  • 55 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12022)

Abstract

For halftone image data hiding, it is difficult to achieve good visual quality and statistical security when high embedding capacity is demanded. In this paper, a secure steganographic scheme for halftone image is proposed, which aims to minimize the embedding distortion on structural similarity. Structural distortions are the ones that affect the most the perception of degradation of a halftone image. To evaluate the structural distortions caused by flipping pixels, halftone image structural similarity (HSSIM) is introduced based on a human visual filter, which is trained by Least-Mean-Square (LMS) approach. Utilizing the HSSIM, a distortion measurement is proposed to evaluate the embedding distortions on both vision and statistics. To minimize the embedding distortions, syndrome-trellis code (STC) is employed in the embedding process. The experimental results have demonstrated that the proposed steganographic scheme can achieve high statistical security with good visual quality without degrading the embedding capacity.

Keywords

Halftone image steganography Distortion measurement Halftone image structural similarity (HSSIM) Syndrome-trellis code (STC) 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. U1736118), the Key Areas R&D Program of Guangdong (No. 2019B010136002), the Key Scientific Research Program of Guangzhou (No. 201804020068), the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), Shanghai Minsheng Science and Technology Support Program (17DZ1205500), Shanghai Sailing Program (17YF1420000), the Fundamental Research Funds for the Central Universities (No. 17lgjc45).

References

  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.
    Bayers, B.: An optimum method for two level rendition of continuous tone pictures. In: Proceedings of the IEEE International Communication Conference, pp. 2611–2615 (1973)Google Scholar
  3. 3.
    Chiew, K.L., Pieprzyk, J.: Binary image steganographic techniques classification based on multi-class steganalysis. In: Kwak, J., Deng, R.H., Won, Y., Wang, G. (eds.) ISPEC 2010. LNCS, vol. 6047, pp. 341–358. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12827-1_25CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  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 by smart pair toggling for halftone images. In: 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings (Cat. No. 00CH37100), vol. 4, pp. 2318–2321. IEEE (2000)Google 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.
    Fu, M.S., Au, O.C.: Data hiding watermarking for halftone images. IEEE Trans. Image Process. 11(4), 477–484 (2002)CrossRefGoogle Scholar
  11. 11.
    Fu, M.S., Au, O.C.L.: Data hiding in halftone images with parity coding. In: Security and Watermarking of Multimedia Contents III, vol. 4314, pp. 360–369. International Society for Optics and Photonics (2001)Google Scholar
  12. 12.
    Goyal, P., Gupta, M., Staelin, C., Fischer, M., Shacham, O., Allebach, J.P.: Clustered-dot halftoning with direct binary search. IEEE Trans. Image Process. 22, 473–487 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Guo, J.M.: Improved data hiding in halftone images with cooperating pair toggling human visual system. Int. J. Imaging Syst. Technol. 17(6), 328–332 (2007)CrossRefGoogle Scholar
  14. 14.
    Guo, J.M., Liu, Y.F.: Halftone-image security improving using overall minimal-error searching. IEEE Trans. Image Process. 20(10), 2800–2812 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Guo, J.M., Liu, Y.F., Chang, J.Y.: High efficient direct binary search using multiple lookup tables. In: 2012 19th IEEE International Conference on Image Processing, pp. 813–816. IEEE (2012)Google Scholar
  16. 16.
    Guo, M., Zhang, H.: High capacity data hiding for halftone image authentication. In: Shi, Y.Q., Kim, H.-J., Pérez-González, F. (eds.) IWDW 2012. LNCS, vol. 7809, pp. 156–168. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40099-5_14CrossRefGoogle Scholar
  17. 17.
    Jarvis, J.F., Judice, C.N., Ninke, W.H.: A survey of techniques for the display of continuous tone pictures on bilevel displays. Comput. Graph. Image Process. 5(1), 13–40 (1976)CrossRefGoogle Scholar
  18. 18.
    Kim, S.H., Allebach, J.P.: Impact of HVS models on model-based halftoning. IEEE Trans. Image Process. 11(3), 258–269 (2002)CrossRefGoogle Scholar
  19. 19.
    Knuth, D.E.: Digital halftones by dot diffusion. ACM Trans. Graph. (TOG) 6(4), 245–273 (1987)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Lien, B.K., Lan, Z.L.: Improved halftone data hiding scheme using Hilbert curve neighborhood toggling. In: International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 73–76 (2011)Google Scholar
  21. 21.
    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
  22. 22.
    Lin, C., Lu, W., Huang, X., Liu, K., Sun, W., Lin, H.: Region duplication detection based on hybrid feature and evaluative clustering. Multimedia Tools Appl. 78(15), 20739–20763 (2019).  https://doi.org/10.1007/s11042-019-7342-9CrossRefGoogle Scholar
  23. 23.
    Lin, C., et al.: Copy-move forgery detection using combined features and transitive matching. Multimedia Tools Appl. 78(21), 30081–30096 (2018).  https://doi.org/10.1007/s11042-018-6922-4CrossRefGoogle Scholar
  24. 24.
    Liu, X., Lu, W., Liu, W., Luo, S., Liang, Y., Li, M.: Image deblocking detection based on a convolutional neural network. IEEE Access 7, 26432–26439 (2019)CrossRefGoogle Scholar
  25. 25.
    Liu, X., Lu, W., Zhang, Q., Huang, J., Shi, Y.Q.: Downscaling factor estimation on pre-JPEG compressed images. IEEE Trans. Circuits Syst. Video Technol. PP, 1 (2019)Google Scholar
  26. 26.
    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. 29, 1608–1618 (2018)CrossRefGoogle Scholar
  27. 27.
    Mannos, J., Sakrison, D.: The effects of a visual fidelity criterion of the encoding of images. IEEE Trans. Inf. Theory 20(4), 525–536 (1974)CrossRefGoogle Scholar
  28. 28.
    Mese, M., Vaidyanathan, P.P.: Optimized halftoning using dot diffusion and methods for inverse halftoning. IEEE Trans. Image Process. 9(4), 691–709 (2000)CrossRefGoogle Scholar
  29. 29.
    Pei, S.C., Guo, J.M.: High-capacity data hiding in halftone images using minimal-error bit searching and least-mean square filter. IEEE Trans. Image Process. 15(6), 1665–1679 (2006)CrossRefGoogle Scholar
  30. 30.
    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. 16, 601–609 (2018)CrossRefGoogle Scholar
  31. 31.
    Yeung, Y., Lu, W., Xue, Y., Huang, J., Shi, Y.Q.: Secure binary image steganography with distortion measurement based on prediction. IEEE Trans. Circuits Syst. Video Technol. PP, 1 (2019)CrossRefGoogle Scholar
  32. 32.
    Zhang, J., Lu, W., Yin, X., Liu, W., Yeung, Y.: Binary image steganography based on joint distortion measurement. J. Vis. Commun. Image Represent. 58, 600–605 (2019)CrossRefGoogle Scholar
  33. 33.
    Zhang, X., Allebach, J.P.: Quad-interleaved block level parallel direct binary search algorithm. Electron. Imaging 2016(20), 1–6 (2016)CrossRefGoogle Scholar

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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