Amplitude-adaptive spread-spectrum data embedding

Amplitude-adaptive spread-spectrum data embedding

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In this study, the authors consider additive spread-spectrum (SS) data embedding in transform-domain host data. Conventional additive SS embedding schemes use an equal-amplitude modulated carrier to deposit one information symbol across a group of host data coefficients which act as interference to SS signal of interest. If there is a flexibility of assigning different amplitudes across symbol bits, the probability of error can be further reduced by adaptively allocating amplitude to each symbol bit based on its own host/interference. In this study, they present a novel amplitude-adaptive SS embedding scheme. Particularly, symbol-by-symbol adaptive amplitude allocation algorithms are developed to compensate for the impact from the known interference. They aim at designing the SS embedding amplitude for each symbol adaptively in order to minimise the receiver bit-error-rate (BER) at any given distortion level. Then, optimised amplitude allocation for multi-carrier/multi-message embedding in the same host data is studied as well. Finally, they consider the problem of amplitude optimisation for an ideal scenario where no external noise is introduced during embedding and transmission. Extensive experimental results illustrate that the proposed amplitude-adaptive SS embedding scheme can provide order-of-magnitude performance improvement over several other state-of-the-art SS embedding schemes.


    1. 1)
    2. 2)
      • 2. Cox, I.J., Miller, M.L., Bloom, J.A.: ‘Digital watermarking’ (Morgan-Kaufmann Press, San Francisco, 2002).
    3. 3)
    4. 4)
    5. 5)
      • 5. Yi, Y., Li, R., Chen, F., et al: ‘A digital watermarking approach to secure and precise range query processing in sensor networks’. Proc. IEEE INFOCOM, Turin, Italy, April 2013, pp. 19501958.
    6. 6)
    7. 7)
    8. 8)
      • 8. Fridrich, J.: ‘Steganography in digital media, principles, algorithms, and applications’ (Cambridge University Press, Cambridge, UK, 2010).
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
      • 26. Gkizeli, M., Pados, D.A., Medley, M.J.: ‘SINR, bit error rate, and Shannon capacity optimized spread-spectrum steganography’. Proc. IEEE Int. Conf. Image Processing (ICIP), Singapore, October 2004, pp. 15611564.
    27. 27)
    28. 28)
    29. 29)
      • 29. Li, M., Kulhandjian, M., Pados, D.A., et al: ‘Steganalysis for spread-spectrum steganography’. Proc. IEEE Int. Conf. Image Processing (ICIP), Brussels, Belgium, September 2011, pp. 19571960.
    30. 30)
    31. 31)
    32. 32)
      • 32. Valizadeh, A., Wang, J.: ‘A framework of multiplicative spread spectrum embedding for data hiding: performance, decoder and signature design’. Proc. IEEE GLOBECOM, Honolulu, HI, December 2009, pp. 16.
    33. 33)
    34. 34)
    35. 35)
      • 35. Boyd, S., Vandenberghe, L.: ‘Convex optimization’ (Cambridge University Press, Cambridge, UK, 2004).
    36. 36)
      • 36. Schaefer, G., Stich, M.: ‘UCID – an uncompressed colour image database’. Proc. SPIE, Storage and Retrieval Methods and Applications for Multimedia, San Jose, CA, January 2004.
    37. 37)
      • 37. Li, W., Zhang, Y., Yang, C.: ‘A survey of JND models in digital image watermarking’. Proc. Int. Conf. Inf. Technology Software Engineering (ITSE), Beijing, China, December 2012, pp. 765774.
    38. 38)

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