Advertisement

Binary Image Steganalysis Based on Local Residual Patterns

  • Ruipeng Li
  • Lingwen Zeng
  • Wei LuEmail author
  • Junjia Chen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 895)

Abstract

A binary image steganalysis scheme based on the statistic model of local residual pattern (LRP) is proposed in this paper. LRP means the pattern of a local area of the residual map of the binary image, which is calculated with the XOR operation. The XOR operation is sensitive to the difference between adjacent pixels, which leads to the emphasis on edge property of the residual map. The neighbouring LRPs of the modified pixel will be affect, which makes the statistic model of LRPs change. Thus the trace of steganography can be detected according to the difference between the statistic models. Finally, the experiments we conducted show that our proposed scheme is effective on binary image steganalysis.

Keywords

Steganalysis Binary image Steganography Machine learning 

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).

References

  1. 1.
    Cao, H., Kot, A.: On establishing edge adaptive grid for bi-level image data hiding. IEEE Trans. Inf. Forensics Secur. 8(9), 1508–1518 (2013)CrossRefGoogle Scholar
  2. 2.
    Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)Google Scholar
  3. 3.
    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
  4. 4.
    Chen, J., Lu, W., Yeung, Y., Xue, Y., Liu, X., Lin, C., Zhang, Y.: Binary image steganalysis based on distortion level co-occurrence matrix. Comput. Mater. Continua 55(2), 201–211 (2018)Google Scholar
  5. 5.
    Chiew, K.L., Pieprzyk, J.: Binary image steganographic techniques classification based on multi-class steganalysis. In: Information Security, Practice and Experience, pp. 341–358. Springer (2010)Google Scholar
  6. 6.
    Chiew, K.L., Pieprzyk, J.: Blind steganalysis: a countermeasure for binary image steganography. In: International Conference on Availability, Reliability and Security. pp. 653–658. IEEE Computer Society, March 2010Google Scholar
  7. 7.
    Chiew, K.L., Pieprzyk, J.: Estimating hidden message length in binary image embedded by using boundary pixels steganography. In: International Conference on Availability, Reliability and Security, pp. 683–688. IEEE Computer Society, March 2010Google Scholar
  8. 8.
    Feng, B., Lu, W., Sun, W.: Binary image steganalysis based on pixel mesh markov transition matrix. J. Vis. Commun. Image Represent. 26, 284–295 (2015)CrossRefGoogle Scholar
  9. 9.
    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 (2015)CrossRefGoogle Scholar
  10. 10.
    Feng, B., Weng, J., Lu, W., Pei, B.: Steganalysis of content-adaptive binary image data hiding. J. Vis. Commun. Image Represent. 46, 119–127 (2017).  https://doi.org/10.1016/j.jvcir.2017.01.008CrossRefGoogle Scholar
  11. 11.
    Fridrich, J.J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7(3), 868–882 (2012)CrossRefGoogle Scholar
  12. 12.
    Guo, M., Zhang, H.: High capacity data hiding for binary image authentication. In: International Conference on Pattern Recognition, pp. 1441–1444. IEEE (2010)Google Scholar
  13. 13.
    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. (2018).  https://doi.org/10.1109/TCSVT.2019.2903432
  14. 14.
    Pevny, T., Bas, P., Fridrich, J.: Steganalysis by subtractive pixel adjacency matrix. IEEE Trans. Inf. Forensics Secur. 5(2), 215–224 (2010)CrossRefGoogle Scholar
  15. 15.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Elsevier Pte Ltd., San Diego (2009)zbMATHGoogle Scholar
  16. 16.
    Wu, M., Liu, B.: Data hiding in binary image for authentication and annotation. IEEE Trans. Multimedia 6(4), 528–538 (2004)CrossRefGoogle Scholar
  17. 17.
    Yang, H., Kot, A.C.: Pattern-based data hiding for binary image authentication by connectivity-preserving. IEEE Trans. Multimedia 9(3), 475–486 (2007)CrossRefGoogle Scholar
  18. 18.
    Yang, H., Kot, A.C., Rahardja, S.: Orthogonal data embedding for binary images in morphological transform domain-a high-capacity approach. IEEE Trans. Multimedia 10(3), 339–351 (2008)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 TechnologyMinistry of Education Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen UniversityGuangzhouChina

Personalised recommendations