Multimedia Tools and Applications

, Volume 71, Issue 3, pp 1499–1528 | Cite as

Condition for energy efficient watermarking without WSS assumption

  • Bin Yan
  • Yin-Jing Guo
  • Xiao-Feng Liu


Energy efficient watermarking preserves the watermark energy after linear attack as much as possible. We consider in this paper the non-stationary signal models and derive conditions for energy efficient watermarking under random vector model without wide sense stationary (WSS) assumption. We find that the covariance matrix of the energy efficient watermark should be proportional to the host covariance matrix to best resist the optimal linear removal attacks. For WSS process model, our result reduces to the well-known power spectrum condition. Intuitive geometric interpretations of the results in Hilbert space of random vectors are discussed, which also provides us simpler proof of the main results. Practical implementation of the covariance matrix shaped watermark for speech signal using linear prediction analysis (LPA) and image signal using eigen-value decomposition (EVD) are also presented and tested, showing improved performance as compared to lowpass and Su’s global watermark.


Energy efficient watermarking Matrix Wiener filter Eigen-Value Decomposition (EVD)  Hilbert space Linear Prediction Analysis (LPA) 



This work is supported by the project of National Natural Science Foundation of China (NSFC) under project grant number: 61272432 . The work of Bin Yan is also supported by Qingdao science and technology development plan (No. 12-1-4-6-(10)-jch). The work of Yin-Jing Guo is supported by the project of NSFC under project grant number: 61071087, and the natural science foundation of Shandong province(ZR2011FM018). The work of Xiao-Feng Liu is supported by the project of NSFC under project grant number: 60905060. The authors would like to thank the anonymous reviewers for their constructive comments and suggestions. We are indebted to the reviewers for their valuable time spent on the manuscript of this paper. The first author would like to thank Prof. Zhe-Ming Lu, Prof. Sheng-He Sun, Prof. Jeng-Shyang Pan and Prof. Xia-Mu Niu for their guidance and help. Their insight in watermarking research have significant influence on this work.


  1. 1.
    Balado F, Whelan KM, Silvestre GCM, Hurley NJ (2005) Joint iterative decoding and estimation for side-informed data hiding. IEEE Trans Signal Process 53:4006–4019. doi: 10.1109/TSP.2005.855412 CrossRefMathSciNetGoogle Scholar
  2. 2.
    Barni J, Bartolini F (2004) Watermarking systems engineering: enabling digital assets security and other applications. Marcel Dekker Inc.Google Scholar
  3. 3.
    Celik M, Sharma G, Tekalp AM (2005) Pitch and duration modification for speech watermarking. In: Proceedings of the 2005 IEEE international conference on acoustics, speech, and signal processing (ICASSP05)Google Scholar
  4. 4.
    Chen B, Wornell GW (2001) Quantization index modulation: a class of provably good methods for digital watermarking and information embedding. IEEE Trans Inf Theory 47(4):1423–1443CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Cheng Q, Sorensen S (2001) Spread spectrum signaling for speech watermarking. In: IEEE international conference on acoustics, speech and signal processing, vol 3, pp 1337–1340Google Scholar
  6. 6.
    Cheng Q, Sorensen S (2005) Spread spectrum signaling for speech watermarking. United States Patent, No. US6892175B1Google Scholar
  7. 7.
    Chu WC (2003) Speech coding algorithms: foundation and evolution of standardized coders. Wiley-Interscience, New JerseyCrossRefGoogle Scholar
  8. 8.
    Coumou DJ, Sharma G (2006) Watermark synchronization for feature-based embedding: application to speech. In: IEEE international conference on multimedia and expo, pp 849–852Google Scholar
  9. 9.
    Coumou DJ, Sharma G (2008) Insertion, deletion codes with feature-based embedding: a new paradigm for watermark synchronization with applications to speech watermarking. IEEE T Inf Foren Sec 3(2):153–165. CrossRefGoogle Scholar
  10. 10.
    Cox I, Miller M, Bloom J, Fridrich J, Kalker T (2007) Digital watermarking and steganography, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco, CAGoogle Scholar
  11. 11.
    Cox IJ, Miller ML, McKellips AL (1999) Watermarking as communication with side information. Proc IEEE 87(7):1127–1141CrossRefGoogle Scholar
  12. 12.
    Deller JR, Hansen JHL, Proakis JG (1993) Discrete-time processing of speech signals. IEEE PressGoogle Scholar
  13. 13.
    Dietl G (2007) Linear estimation and detection in Krylov subspaces. SpringerGoogle Scholar
  14. 14.
    Gang L, Akansu AN, Ramkumar M (2002) Security and synchronization in watermark sequence. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, vol 4, pp 3736–3739. Orlando, Florida, USAGoogle Scholar
  15. 15.
    Gerbrands JJ (1981) On the relationships between SVD, KLT and PCA. Pattern Recogn 14(1C6):375–381CrossRefMATHMathSciNetGoogle Scholar
  16. 16.
    Gray RM (2005) Toeplitz and circulant matrices: a review. Commun Inf Theory 2:155–239Google Scholar
  17. 17.
    Hofbauer K, Kubin G, Kleijn WB (2009) Speech watermarking for analog flat-fading bandpass channels. IEEE Trans Audio Speech Lang Process 17:1624–1637CrossRefGoogle Scholar
  18. 18.
    Hwang Y, Moon KA, Kim MJ (2002) Watermark design for the Wiener attack and whitening filtered detectioin. In: Proceedings of the SPIE, vol 4675, pp 441–449Google Scholar
  19. 19.
    Jain AK (1989) Fundamentals of digital image processing. Prentice-Hall Inc., Upper Saddle River, NJMATHGoogle Scholar
  20. 20.
    Jayant N, Noll P (1984) Digital coding of waveforms: principles and applications to speech and video. Prentice-Hall signal processing series, Prentice-HallGoogle Scholar
  21. 21.
    Jeebananda P, Manish K (2011) Application of energy efficient watermark on audio signal for authentication. In: 2011 international conference on computational intelligence and communication networks (CICN), pp 202–206Google Scholar
  22. 22.
    Kay S (1993) Fundamentals of statistical signal processing: estimation theory. Prentice HallGoogle Scholar
  23. 23.
    Kay S (1998) Fundamentals of statistical signal processing: detection theory. Prentice HallGoogle Scholar
  24. 24.
    Kay SM (1988) Modern spectral estimation: theory and application. Prentice-Hall, Englewood Cliffs, NJMATHGoogle Scholar
  25. 25.
    Kejariwal A, Gupta S, Nicolau A, Dutt ND, Gupta RK (2006) Energy efficient watermarking on mobile devices using proxy-based partitioning. IEEE Trans Very Large Scale Integr (VLSI) Syst 14(6):625–636CrossRefGoogle Scholar
  26. 26.
    Lee LT (2009) An energy efficient and high security watermarking algorithm based on DS-CDMA. Master’s thesis, National Taiwan University of Science and TechnologyGoogle Scholar
  27. 27.
    Luenberger DG (1997) Optimization by vector space methods. Wiley-InterscienceGoogle Scholar
  28. 28.
    Moulin P, Ivanovic A, Ivanovic A (2001) Game-theoretic analysis of watermark detection. In: IEEE int. conf. on image processing. ThessalonikiGoogle Scholar
  29. 29.
    Moulin P, Oapos Sullivan JA (2003) Information-theoretic analysis of information hiding. IEEE Trans Inf Theory 49(3):563–593CrossRefMATHGoogle Scholar
  30. 30.
    Orfanidi SJ (2007) SVD, PCA, KLT, CCA, and all that. Online.
  31. 31.
    Pai YT, Ruan SJ, Götze J (2005) Energy-efficient watermark algorithm based on pairing mechanism. In: Proc. 9th int. conf. on knowledge-based intelligent information & engineering systems (KES2005), vol 3681, pp 1219–1225. Melbourne, AustraliaGoogle Scholar
  32. 32.
    Painter T, Spanias A (2000) Perceptual coding of digital audio. Proc IEEE 88:451–515CrossRefGoogle Scholar
  33. 33.
    Pateux S, Guelvouit GL (2002) Practical watermarking scheme based on wide spread spectrum and game theory. In: Elsevier: signal processing-image communication, pp 283–296Google Scholar
  34. 34.
    Perez-Gonzalez F, Balado F, Martin JH (2003) Performance analysis of existing and new methods for data hiding with known-host information in additive channels. IEEE Trans Signal Process 51(4):960–980CrossRefMathSciNetGoogle Scholar
  35. 35.
    Quatieri TF (2002) Discrete-time speech signal processing: principles and practice. Prentice HallGoogle Scholar
  36. 36.
    Sadasivam S, Moulin P, Coleman TP (2011) A message-passing approach to combating desynchronization attacks. IEEE Trans Info Forensics Security 6(3):894–905CrossRefGoogle Scholar
  37. 37.
    Sakaguchi S, Arai T, Murahara Y (2000) The effect of polarity inversion of speech on human perception and data hiding as an application. In: IEEE international conference on acoustics speech and signal processing, vol 2, pp 917–920Google Scholar
  38. 38.
    Sanei S, Chambers J (2007) EEG signal processing. John Wiley & Sons.
  39. 39.
    Schonemann PH (1985) On the formal differentiation of traces and determinants. Multivariate Behav Res 20(2):113–139CrossRefGoogle Scholar
  40. 40.
    Sequeira A, Kundur D (2001) Communication and information theory in watermarking: a survey. In: Proc. SPIE, pp 216–227Google Scholar
  41. 41.
    Su JK, Girod B (1999) On the robustness and imperceptibility of digital fingerprints. In: Proceedings of the 1999 IEEE international conference on multimedia computing and systems, pp 530–535Google Scholar
  42. 42.
    Su JK, Girod B (1999) Power spectrum condition for energy-efficient watermarking. In: Proceedings of the 1999 International Conference on Image Processing (ICIP-99), pp 301–305Google Scholar
  43. 43.
    Su JK, Girod B (2002) Power-spectrum condition for energy-efficient watermarking. IEEE Trans Multimedia 4(4):551–560CrossRefGoogle Scholar
  44. 44.
    Tsiatis AA (2006) Semiparametric theory and missing data. Springer Science Business MediaGoogle Scholar
  45. 45.
    Wang H, Wang W, Chen M, Yao X (2011) Quality-driven secure audio transmissions in wireless multimedia sensor networks. Multimed Tools Appl 1–17. doi: 10.1007/s11042-011-0928-5
  46. 46.
    Yan B, Guo YJ (2011) Hidden information rate of information hiding system based on spread spectrum with covariance matrix shaping. ICIC-EL 5(7):2179–2184MathSciNetGoogle Scholar
  47. 47.
    Yan B, Guo YJ, Wang XM (2010) Performance of spread spectrum watermarking in autoregressive host model under additive white gaussian noise channel. Journal of Measurement Science and Instrumentation 1(3):271–275Google Scholar
  48. 48.
    Yan B, Guo YJ, Wang XM (2011) Capacity of additive colored noise watermarking channel with perceptual distortion measure. In: Proceeding of 1st international conference on pervasive computing signal processing and applications, pp 1009–1012Google Scholar
  49. 49.
    Yan B, Lu ZM, Sun SH (2006) Security of autoregressive speech watermarking model under guessing attack. IEEE T Inf Foren Sec 1(3):386–390CrossRefGoogle Scholar
  50. 50.
    Yan B, Lu ZM, Sun SH (2007) Spectrum shaped dither modulation watermarking for correlated host signal. In: IEEE international conference on multimedia and expo, vol 1, pp 1227–1230Google Scholar
  51. 51.
    Zue V, Seneff S, Glass J (1990) Speech database development at MIT: TIMIT and beyond. Speech Commun 9(4):351–356CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.Department of Communication Engineering, School of Information and Electrical EngineeringShandong University of Science and TechnologyQingdaoPeople’s Republic of China
  2. 2.College of Computer and Information Engineering, Changzhou CampusHohai UniversityNanjingPeople’s Republic of China

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