Multimedia Tools and Applications

, Volume 76, Issue 5, pp 6821–6842 | Cite as

Steganography of digital watermark by Arnold scrambling transform with blind source separation morphological component analysis



In this paper, a novel steganography of digital watermark scheme which contains digital watermark embedding and extraction processes is proposed. The proposed scheme is based on an iterative process of Arnold scrambling transform which is controlled by secret key shared by copyright owner and authorized users, and the extension of morphological component analysis theory which utilizes morphological diversity as the kernel role in blind source separation. Since both the original cover image and watermark are not needed for recovering received watermark, the proposed scheme can be categorized into blind watermarking technique. It is the most applicable watermarking technique due to the availability of the original cover image and watermark cannot be warranted in real-world applications. The proposed scheme overcomes the problem of too narrow hidden data bandwidth in traditional Least-Significant-Bit (LSB) replacement or LSB matching schemes. Instead of only hiding limited bits is allowed, we can embed a meaningful picture into the cover image by applying proposed scheme. Compared with classic Joint Photographic Experts Group (JPEG) steganography schemes, the proposed steganography scheme has much higher embedding capacity and broader applicability scope. Images acquired in steganography experiments and the analysis of experimental results both prove the effectiveness of proposed scheme. Both subjective visual effect and objective quantitative results of the Peak Signal to Noise Ratio (PSNR) index, Structural Similarity (SSIM) index and Normalized Correlation (NC) index confirm its brilliant steganography capability as well as its fine robustness to different noise attacks through communication channel.


Arnold scrambling transform Blind source separation Digital watermark Morphological component analysis Secret key Steganography 


  1. 1.
    Bender W, Gruhl D, Morimoto N, Lu A (1996) Techniques for data hiding. IBM Syst J 35(3.4):313–336CrossRefGoogle Scholar
  2. 2.
    Bobin J, Starck J-L, Fadili J, Moudden Y (2007) Sparsity and morphological diversity in blind source separation. IEEE Trans Image Process 16(11):2662–2674MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Dumitrescu S, Wu X, Wang Z (2003) Detection of LSB steganography via sample pair analysis. IEEE Trans Signal Process 51(7):1995–2007CrossRefMATHGoogle Scholar
  4. 4.
    Elad M, Starck J-L, Querre P, Donoho DL (2005) Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA). Appl Comput Harmon Anal 19(3):340–358MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Fridrich J, Goljan M, Du R (2001) Detecting LSB steganography in color, and gray-scale images. IEEE Multimedia 8(4):22–28CrossRefGoogle Scholar
  6. 6.
    Fridrich J, Goljan M, Du R (2001) Steganalysis based on JPEG compatibility, SPIE multimedia systems and applications IV. SPIE Press, BellinghamGoogle Scholar
  7. 7.
    Hetzl S, Mutzel P (2005) A graph theoretic approach to steganography. In Proceedings of 9th IFIP TC-6 TC-11 International Conference of Communications and Multimedia Security, vol. 3677, pp. 119–128Google Scholar
  8. 8.
    Huang F, Huang J, Shi Y-Q (2012) New channel selection rule for JPEG steganography. IEEE Trans Inf Forensic Secur 7(4):1181–1191CrossRefGoogle Scholar
  9. 9.
    Luo W, Huang F, Huang J (2010) Edge adaptive image steganography based on LSB matching revisited. IEEE Trans Inf Forensic Secur 5(2):201–214CrossRefGoogle Scholar
  10. 10.
    Petitcolas FAP, Anderson RJ, Kuhn MG (1999) Information hiding — a survey. Proc IEEE 87(7):1062–1078CrossRefGoogle Scholar
  11. 11.
    Sallee P (2003) Model based steganography. In Proceedings of International Workshop on Digital Watermarking, pp. 174–188Google Scholar
  12. 12.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):660–612CrossRefGoogle Scholar
  13. 13.
    Westfeld A (2001) F5 — A steganographic algorithm high capacity despite better steganalysis. In Proceedings of 4th International Workshop on Information Hiding, vol. 1768, pp. 289–302Google Scholar
  14. 14.
    Yang C, Liu F, Luo X, Liu B (2008) Steganalysis frameworks of embedding in multiple least-significant bits. IEEE Trans Inf Forensic Secur 3(4):662–672CrossRefGoogle Scholar
  15. 15.
    Zhang T, Ping X (2003) A new approach to reliable detection of LSB steganography in natural images. Signal Process 83(10):2085–2093CrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Software and Service GroupAsia-Pacific Research and Development Ltd, IntelShanghaiChina

Personalised recommendations