Advertisement

Distortion Function for Steganography in Texture Synthesized Images

  • Lina Shi
  • Zichi Wang
  • Zhenxing Qian
  • Xinpeng Zhang
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 109)

Abstract

This paper proposes a distortion function for steganography in texture synthesized images. Given a small piece of texture, an image synthesis algorithm is employed to generate a texture image in arbitrary size with similar local appearance. The obtained texture image is used as cover for data embedding. A distortion function is designed for the cover image to measure the detection risk of modifications. The image texture, splicing of patches, and repetition of texture blocks are contained in the proposed distortion function to fit the properties of synthesized images, which results in high undetectability against steganalysis. Experimental results also prove that the proposed distortion function performs better than current state-of-the-art steganographic methods.

Keywords

Steganography Texture synthesis Distortion function 

Notes

Acknowledgments

This work was supported by the Natural Science Foundation of China (U1536108, 61572308, U1636206, U1736213), the Shanghai Dawn Scholar Plan (14SG36), and the Shanghai Excellent Academic Leader Plan (16XD1401200).

References

  1. 1.
    Li, B., Wang, M., Li, X., Tan, S., Huang, J.: A strategy of clustering modification directions in spatial image steganography. IEEE Trans. Inf. Forensics Secur. 10(9), 1905–1917 (2015)CrossRefGoogle Scholar
  2. 2.
    Zhang, Y., Qin, C., Zhang, W., Liu, F., Luo, X.: On the fault-tolerant performance for a class of robust image steganography. Sig. Process. 146, 99–111 (2018)CrossRefGoogle Scholar
  3. 3.
    Wang, Z., Zhang, X., Yin, Z.: Hybrid distortion function for JPEG steganography. J. Electron. Imaging 25(5), 050501 (2016)CrossRefGoogle Scholar
  4. 4.
    Frdrich, J., Soukal, D.: Matrix embedding for large payloads. In: Proceedings of the International Society for Optics and Photonics, San Jose, CA, Feb. 2006, pp. 60721W–60721W-12Google Scholar
  5. 5.
    Zhang, X., Wang, S.: Efficient steganographic embedding by exploiting modification direction. IEEE Commun. Lett. 10(11), 781–783 (2006)CrossRefGoogle Scholar
  6. 6.
    Zhang, W., Zhang, X., Wang, S.: Maximizing steganographic embedding efficiency by combining hamming codes and wet paper codes. In: Proceedings of 10th International Workshop on Information Hiding, Santa Barbara, CA, USA, May, 2008, pp. 60–71Google Scholar
  7. 7.
    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
  8. 8.
    Holub, V., Fridrich, J.: Digital image steganography using universal distortion. In: Proceedings of the first ACM workshop on Information Hiding and Multimedia Security, New York, NY, USA, June, 2013, pp. 59–68Google Scholar
  9. 9.
    Holub, V., Fridrich, J., Denemark, T.: Universal distortion function for steganography in an arbitrary domain. EURASIP J. Inf. Secur. 2014(1), 1–13 (2014)CrossRefGoogle Scholar
  10. 10.
    Li, B., Wang, M., Huang, J., Li, X.: A new cost function for spatial image steganography. In: Proceedings of IEEE International Conference on Image Processing, Paris, France, Oct. 2014, pp. 4206–4210Google Scholar
  11. 11.
    Otori, H., Kuriyama, S.: Data-embeddable texture synthesis. In: Proceedings of the 8th International Symposium Smart Graphics, Kyoto, Japan, 2007, pp. 146–157Google Scholar
  12. 12.
    Otori, H., Kuriyama, S.: Texture synthesis for mobile data communications. IEEE Comput. Graph. Appl. 29(6), 74–81 (2009)CrossRefGoogle Scholar
  13. 13.
    Cohen, M.F., Shade, J., Hiller, S., Deussen, O.: Wang tiles for image and texture generation. ACM Trans. Graph. 22(3), 287–294 (2003)CrossRefGoogle Scholar
  14. 14.
    Xu, K., et al.: Feature-aligned shape texturing. ACM Trans. Graph. 28(5), Art. ID 108 (2009)Google Scholar
  15. 15.
    Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: Conference on Computer Graphics and Interactive Techniques. ACM, 2001, pp. 341–346Google Scholar
  16. 16.
    Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)CrossRefGoogle Scholar
  17. 17.
    Kodovsky, J., Fridrich, J., Holub, V.: Ensemble classifiers for steganalysis of digital media. IEEE Trans. Inf. Forensics Secur. 7(2), 432–444 (2012)CrossRefGoogle Scholar
  18. 18.
    Qian, Z., Huang, N., Li, S., Zhang, X.: Constructive steganography using texture synthesis. IETE Tech. Rev.  https://doi.org/10.1080/02564602.2018.1475267
  19. 19.
    Qian, Z., Zhou, H., Zhang, W., Zhang, X.: Robust steganography using texture synthesis. In: Twelfth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2016, pp. 25–33, Kaohsiung, Taiwan, Nov. 21–23, 2016Google Scholar
  20. 20.
    Zhou, H., Chen, K., Zhang, W., Qian, Z., Yu, N.: Targeted attack and security enhancement on texture synthesis based steganography. J. Vis. Commun. Image Represent.  https://doi.org/10.1016/j.jvcir.2018.04.011CrossRefGoogle Scholar
  21. 21.
    Wu, K.C., Wang, C.M.: Steganography using reversible texture synthesis. IEEE Trans. Image Process. 24(1), 130 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lina Shi
    • 1
  • Zichi Wang
    • 1
  • Zhenxing Qian
    • 1
  • Xinpeng Zhang
    • 1
  1. 1.School of Communication and Information EngineeringShanghai UniversityShanghaiPeople’s Republic of China

Personalised recommendations