Advertisement

Performance Analysis of Invariant Quaternion Moments in Color Image Watermarking

  • Khalid M. Hosny
  • Mohamed M. Darwish
Chapter

Abstract

In the last decade, the invariant quaternion moment-based watermarking methods were successfully used due to their robustness against the geometric attacks. In this chapter, a performance analysis of the invariant quaternion moment-based methods for color image watermarking is presented. An extensive study of the color image watermarking using a set of quaternion moments. In this comparative study, a unified numerically stable method is utilized for computing accurate quaternion color moments in polar coordinates where the angular kernel is computed over circular pixels using analytical integration. The radial kernels are computed using accurate Gaussian quadrature method. In these watermarking methods, better characteristics of the various quaternion moments such as their capabilities in reconstructing high quality watermarked images, computational complexity, accuracy and stability are considered. Moreover, evaluation criteria are selected carefully to evaluate the performance of the watermarking methods in terms of visual imperceptibility and robustness against different attacks. Experiments are performed where the obtained results are used to analyze the performance of the various quaternion moment-based watermarking methods.

Keywords

Quaternion moments Color image watermarking Geometric attacks 

References

  1. 1.
    Sun ZZ, Zhang QX, Li YA, Tan YZ (2016) DPPDL: a dynamic partial-parallel data layout for green video surveillance storage. IEEE Trans Circuits Syst Video Technol. doi:10.1109/TCSVT.2016.2605045Google Scholar
  2. 2.
    J. Yu, B. Zhang, Z. Kuang, D. Lin, J. Fan, I “privacy: image privacy protection by identifying sensitive objects via deep multi-task Learning:, IEEE Trans. Inf. Forensics Secur. 12 (5) (2017) 1005-1016.CrossRefGoogle Scholar
  3. 3.
    A. Khan, A. Siddiqa, S. Munib, S.A. Malik, A recent survey of reversible watermarking techniques, Inf Sci (NY) 279 (2014) 251-272.CrossRefGoogle Scholar
  4. 4.
    Atawneh S, Almomani A, Al Bazar H et al (2016) Secure and imperceptible digital image steganographic algorithm based on diamond encoding in DWT domain. Multimedia Tools and Applications. doi:10.1007Google Scholar
  5. 5.
    Memos VA, Psannis KE (2016) Encryption algorithm for efficient transmission of hevc media. J Real-Time Image Proc 12(2):473-482CrossRefGoogle Scholar
  6. 6.
    Shih FY et al (2008) Digital Watermarking and Steganography: Fundamentals and Techniques, Taylor & Francis Group. CRC Press., Inc., Boca RatonGoogle Scholar
  7. 7.
    W.H. Ren, X. Li, and Z.M. Lu, "Reversible Data Hiding Scheme based on Fractal Image Coding," Journal of Information Hiding and Multimedia Signal Processing, vol. 8, no. 3, pp. 544-550, May 2017.Google Scholar
  8. 8.
    Z. Shokrollahi and M. Yazdi, "A Robust Blind Watermarking Scheme Based on Stationary Wavelet Transform," Journal of Information Hiding and Multimedia Signal Processing, vol. 8, no. 3, pp. 676-687, May 2017.Google Scholar
  9. 9.
    S.C. Chu, H.C. Huang, Y. Shi, S.Y. Wu, and C.S. Shieh, "Genetic Watermarking for Zerotree-based Applications," Circuits, Systems, and Signal Processing, vol. 27, no. 2, pp. 171-182, Apr. 2008.Google Scholar
  10. 10.
    A.K. Singh, B. Kumar, M. Dave, A. Mohan Robust and imperceptible dual watermarking for telemedicine applications. WirelPersCommun 80(4): (2014) 1415–1433CrossRefGoogle Scholar
  11. 11.
    D.S. Chauhan, A.K. Singh, B. Kumar, J.P. Saini (2017) Quantization based multiple medical information watermarking for secure e-health, multimedia tools and applications, pp 1–13. https://doi.org/10.1007/s11042-017-4886-4CrossRefGoogle Scholar
  12. 12.
    A. K. Singh, “Improved Hybrid Technique for Robust and Imperceptible Multiple Watermarking using Medical Images, Multimedia Tools and Applications, Vol. 76, Issue 6, pp 8881-8900 Springer US, 10.1007/s11042-016-3514-z.Google Scholar
  13. 13.
    A.K. Singh, B. Kumar, A. Mohan, “Medical image watermarking: techniques and applications”, book series on multimedia systems and applications. Springer, USA, 2017.CrossRefGoogle Scholar
  14. 14.
    A. Zear, A.K. Singh, P. Kumar, A proposed secure multiple watermarking technique based on DWT, DCT and SVD for application in medicine, Multimedia Tools Appl. (2016)  https://doi.org/10.1007/s11042-016-3862-8.CrossRefGoogle Scholar
  15. 15.
    S. Thakur, A. K. Singh, S. P. Ghrera, A. Mohan, Chaotic based secure watermarking approach for medical images, Multimedia Tools and Applications, Springer DOI: 10.1007/s11042-018-6691-0.Google Scholar
  16. 16.
    A. K. Singh, B. Kumarb, S. K. Singh c, S.P. Ghrera, A. Mohan, Multiple Watermarking Technique for Securing Online Social Network Contents using Back Propagation Neural Network, Future Generation Computer Systems, pp.1-16 DOI: 10.1016/j.future.2016.11.023CrossRefGoogle Scholar
  17. 17.
    C. Kumar, A. K. Singh, and P. Kumar, “A recent survey on image watermarking techniques and its application in e-governance”, Multimedia Tools and Applications, Springer DOI: 10.1007/s11042-017-5222-8.CrossRefGoogle Scholar
  18. 18.
    K.-L. Chung, C.-Y. Chiu, T.-Y. Yu, P.-L. Huang, “Temporal and spatial correlation-based reversible data hiding for RGB CFA videos, Inf Sci (NY) 420 (Sup- plement C) (2017) 386–402.Google Scholar
  19. 19.
    R. Srivastav, B. Kumar, A. K. Singh and Anand Mohan, “computationally efficient joint imperceptible image watermarking and JPEG compression: A green computing approach, Multimedia Tools and Applications, Springer US DOI: 10.1007/s11042-017-5214-8CrossRefGoogle Scholar
  20. 20.
    L. Singh, A. K. Singh, P. K. Singh, “Secure data hiding techniques: A survey”, Multimedia Tools and Applications, Springer DOI: 10.1007/s11042-018-6407-5.Google Scholar
  21. 21.
    T. Huynh-The, O. Banos, S. Lee, Y. Yoon, T. Le-Tien, “Improving digital image watermarking by means of optimal channel selection”, Expert Syst. Appl. 62 (2016) 177–189.CrossRefGoogle Scholar
  22. 22.
    E. Tsougenis, G. Papakostas, D. Koulouriotis, E. Karakasis, “Adaptive color image watermarking by the use of quaternion image moments”, Expert Syst. Appl. 41 (14) (2014) 6408–6418.CrossRefGoogle Scholar
  23. 23.
    C. Kumar, A.K. Singh, P. Kumar, R. Singh, S. Singh, “SPIHT based multiple image watermarking in NSCT domain”, Concurrency and Computation: Practice and Experience, Wiley, DOI:10.1002/cpe.4912.Google Scholar
  24. 24.
    A.M. Abdelhakim, H.I. Saleh, A.M. Nassar, “A quality guaranteed robust image watermarking optimization with artificial bee colony”, Expert Syst. Appl. 72 (2017) 317–326CrossRefGoogle Scholar
  25. 25.
    P.-Y. Lin, J.-S. Lee, and C.-C. Chang., “Protecting the content integrity of digital imagery with fidelity preservation”. ACM Trans. Multimedia Comput. Commun. Appl., Article 15, Vol. 7 (3), 20 pages, 2011.CrossRefGoogle Scholar
  26. 26.
    M. Yu, J. Wang, G. Jiang, Z. Peng, F. Shao, T. Luo, “New fragile watermarking method for stereo image authentication with localization and recovery”. AEU Int. J Electron Commun., Vol. 69(1), p. 361–370, 2015.CrossRefGoogle Scholar
  27. 27.
    12. Petitcolas F, Anderson R, Kuhn M (1998) Attacks on copyright marking systems, LNCS, 218–238Google Scholar
  28. 28.
    Masoud Alghoniemy and Ahmed H. Tewfik, “Geometric invariance in image watermarking,” IEEE Transactions on Image Processing, vol. 13, no. 2, pp. 145-153, 2004.CrossRefGoogle Scholar
  29. 29.
    Y. Xin, S. Liao, and M. Pawlak, “Circularly orthogonal moments for geometrically robust image watermarking”, Pattern Recognition, Vol. 40, p. 3740–3752, 2007.zbMATHCrossRefGoogle Scholar
  30. 30.
    I. A. Ismail, M. A. Shouman, K. M. Hosny, H. M. Abdel-Salam “Invariant image watermarking using accurate Zernike moments”, Journal Computer Science, 2010, 6, (1), pp. 52–59CrossRefGoogle Scholar
  31. 31.
    H. Q. Zhu, M. Liu, and Y. Li, “The RST invariant digital image watermarking using Radon transforms and complex moments,” Digital Signal Processing, vol. 20, no. 6, pp. 1612–1628, 2010.CrossRefGoogle Scholar
  32. 32.
    H. Zhang, H. Shu, G. Coatrieux, Affine Legendre moment invariants for image watermarking robust to geometric distortions, IEEE Trans. Image Process., Vol. 20(8), p. 2189–2199, 2011.Google Scholar
  33. 33.
    L. Li et al., “Geometrically invariant image watermarking using Polar Harmonic Transforms,” Inform. Sci. 199, 1–19 (2012).MathSciNetzbMATHCrossRefGoogle Scholar
  34. 34.
    Papakostas GA, Koulouriotis DE, Tourassis VD. “Performance evaluation of moment-based watermarking methods: a review. J SystSoftw 2012;85(8):1864–84.Google Scholar
  35. 35.
    E.D. Tsougenis, G.A. Papakostas, D.E. Koulouriotis, V.D. Tourassis, “Towards adaptivity of image watermarking in polar harmonic transforms domain”, Optics & Laser Technology, Vol. 54, p. 84-97, 2013.Google Scholar
  36. 36.
    H.Y. Yang, X.Y. Wang, P. Wang, P.P. Niu, “Geometrically resilient digital watermarking scheme based on radial harmonic Fourier moments magnitude”, AEU - Int. J. Electron. Commun., Vol. 69, p. 389–399, 2015.Google Scholar
  37. 37.
    Wang Chun-peng, Wang Xing-yuan, Xia Zhi-qiu, “Geometrically invariant image watermarking based on fast Radial Harmonic Fourier Moments”, Signal Processing: Image Communication, Vol. 45, p. 10–23, 2016.Google Scholar
  38. 38.
    Qi M, Li BZ, Sun H. “Image watermarking using polar harmonic transform with parameters in SL(2, R). Signal Processing: Image Communication 2015; 31:161–73Google Scholar
  39. 39.
    K. M. Hosny and M. M. Darwish, “Invariant image watermarking using accurate Polar Harmonic transforms”, Computers and Electrical Engineering, Vol.62, p.429-447, 2017.CrossRefGoogle Scholar
  40. 40.
    T.K. Tsui, X.P. Zhang, D. And routsos, “Color image watermarking using multidimensional Fourier transforms”, IEEE Trans. Inform. Forensics Secur., Vol. 3 (1), p.16–28, 2008.CrossRefGoogle Scholar
  41. 41.
    J.A. Hussein, “Luminance-based embedding approach for color image watermarking”, Int. J. Image Graph. Signal Process., Vol. 4 (3), p.49–56, 2012.CrossRefGoogle Scholar
  42. 42.
    H. Peng, J. Wang, W.X. Wang, Image watermarking method in multi-wavelet domain based on support vector machines, J. Syst. Softw., Vol. 83(8), p. 1470–1477, 2010.CrossRefGoogle Scholar
  43. 43.
    P.P. Niu, X.Y. Wang, Y.P. Yang, M.Y. Lu, “A novel color image watermarking scheme in non-sampled contourlet domain”, Expert Syst. Appl., Vol. 38(3), p. 2081–2098, 2011.Google Scholar
  44. 44.
    Liu. Kuo-Cheng, “Wavelet-based watermarking for color images through visual masking”, AEU-Int. J. Electron. Commun., Vol. 64 (2), p. 112–124, 2010.CrossRefGoogle Scholar
  45. 45.
    C.H. Chou, K.C. Liu, “A perceptually tuned watermarking scheme for color images”, IEEE Trans. Image Process., Vol. 19 (11), p. 2966–2982, 2010.Google Scholar
  46. 46.
    A. Basso, D. Cavagnino, V. Pomponiu, “Blind watermarking of color images using Karhunen–Loève transform keying”, Comput. J. 54 (7) (2011) 1076–1090.CrossRefGoogle Scholar
  47. 47.
    Q. Su, Y. Niu, X. Liu, “A blind dual color images watermarking based on IWT and state coding, Opt. Commun. 285 (7) (2012) 1717–1724.CrossRefGoogle Scholar
  48. 48.
    S. Roy, A. K. Pal, “A blind DCT based color watermarking algorithm for embedding multiple watermarks, Int. J. Electron. Commun. (AEÜ) 72 (2017) 149–161CrossRefGoogle Scholar
  49. 49.
    Q. Su and B. Chen, “A novel blind color image watermarking using upper Hessenberg matrix”, Int. J. Electron. Commun. (AEÜ) 78 (2017) 64–71.CrossRefGoogle Scholar
  50. 50.
    FindIk O, Babaoglu I, Ülker E. A color image watermarking scheme based on artificial immune recognition system. Expert SystAppl 2011;38(3):1942–6.Google Scholar
  51. 51.
    Niu PP, Wang XY, Yang YP, Lu MY. A novel color image watermarking scheme in non-sampled contourlet-domain. Expert SystAppl 2011;38(3):2081–98.CrossRefGoogle Scholar
  52. 52.
    Vahedi E, Zoroofi RA, Shiva M. Toward a new wavelet-based watermarking approach for color images using bio-inspired optimization principles. Digital Signal Process 2012;22(1):153–62.CrossRefGoogle Scholar
  53. 53.
    Wang X, Wang C, Yang H, Niu P. A robust blind color image watermarking in quaternion Fourier transform domain. J SystSoftw 2013;86(2):255–77.CrossRefGoogle Scholar
  54. 54.
    Shao Z, Duan Y, Coatrieux G, Wu J, Meng J, Shu H. Combining double random phase encoding for color image watermarking in quaternion gyrator domain. OptCommun 2015; 343:56–65.CrossRefGoogle Scholar
  55. 55.
    Su Q, Niu Y, Zou H, Zhao Y, Yao T. A blind double color image watermarking algorithm based on QR decomposition. Multimedia Tools App 2014;72 (1):987–1009.CrossRefGoogle Scholar
  56. 56.
    Chen B, Coatrieux G, Chen G, Sun X, Coatrieux JL, Shu H. Full 4-D quaternion discrete Fourier transform based watermarking for color images. Digital Signal Process 2014;28(5):106–19.CrossRefGoogle Scholar
  57. 57.
    B. Chen, C. Zhou, B. Jeon3, Y. Zheng, J. Wang, Quaternion discrete fractional random transform for color image adaptive watermarking, Multimed Tools Appl (2017)Google Scholar
  58. 58.
    Tsougenis ED, Papakostas G A, Koulouriotis DE, Karakasis EG, “Adaptive color image watermarking by the use of quaternion image moments”, Expert Syst Appl., Vol. 41(14), p.6408–6418, 2014.CrossRefGoogle Scholar
  59. 59.
    Wang XY, Niu PP, Yang HY, Wang CP, Wang AL, “A new robust color image watermarking using local quaternion exponent moments”. Inf. Sci. Vol. 277, pp.731–754, 2014.CrossRefGoogle Scholar
  60. 60.
    H. Yang, Y. Zhang, P. Wang, X. Wang, C. Wang, “A geometric correction based robust color image watermarking scheme using quaternion Exponent moments”, Optik, Vol.125, pp.4456–4469, 2014.Google Scholar
  61. 61.
    X.Y. Wang, H.Y. Yang, P.P. Niu, C.P. Wang, “Quaternion exponent moments based robust color image watermarking”, J. Comput. Res. Dev., Vol. 53, p. 651–665, 2016.Google Scholar
  62. 62.
    P. Niu, P. Wang, Y. Liu, H. Yang, X. Wang, “Invariant color image watermarking approach using quaternion radial harmonic Fourier moments”, Multimed. Tools Appl. (2015).Google Scholar
  63. 63.
    H.Y. Yang, X.Y. Wang, P.P. Niu, A.L. Wang, “Robust color image watermarking using geometric invariant quaternion polar harmonic transform”, ACM Trans. Multimed Comput. Commun. Appl. 11 (3) 1-26, 2015.Google Scholar
  64. 64.
    Wang XY, Liu YN, Han MM, Yang HY, “Local quaternion PHT based robust color image watermarking algorithm”, J. Vis Commun Image Represent, Vol. 38, pp.678–694, 2016.CrossRefGoogle Scholar
  65. 65.
    Khalid M. Hosny and Mohamed M. Darwish, “Robust Color Image Watermarking Using Invariant Quaternion Legendre-Fourier Moments”, Multimedia Tools and Applications, Vol. 77, Issue 19, pp 24727–24750, 2018.CrossRefGoogle Scholar
  66. 66.
    W.R. Hamilton, “Elements of Quaternions”, Longmans Green, London, U.K., 1866.Google Scholar
  67. 67.
    T.A. Ell, S.J. Sangwine, “Hypercomplex Fourier transforms of color images”, IEEE Transaction of Image Process, Vol. 16, p. 22–35, 2007.MathSciNetzbMATHCrossRefGoogle Scholar
  68. 68.
    B. J. Chen, H. Z. Shu, H. Zhang, G. Chen, C. Toumoulin, J. L. Dillenseger, and L. M. Luo, “Quaternion Zernike moments and their invariants for color image analysis and object recognition”, Signal Processing, Vol. 92 (2), p. 308-318, 2012.CrossRefGoogle Scholar
  69. 69.
    Chen, B.-J., et al., Color face recognition using quaternion representation of color image. ACTA AutomaticaSinica, 2012. 38(11): p. 1815-1823.MathSciNetzbMATHCrossRefGoogle Scholar
  70. 70.
    L.Q. Guo, M. Zhu, Quaternion Fourier Mellin moments for color images, Pattern Recognit. 44 (2011) 187–195.zbMATHCrossRefGoogle Scholar
  71. 71.
    B. Xiao, G. Wang, W. Li, “Radial Shifted Legendre Moments for Image Analysis and Invariant Image Recognition”, Image and Vision Computing, Vol.32 (12), p. 994-1006, 2014.CrossRefGoogle Scholar
  72. 72.
    Khalid M. Hosny and Mohamed M. Darwish, “New Set of Quaternion Moments for Color Images Representation and Recognition”, Journal of Mathematical Imaging and Vision, Vol. 60, p. 717–736, 2018.MathSciNetzbMATHCrossRefGoogle Scholar
  73. 73.
    P. Yap, X. Jiang and A.C. Kot, "Two Dimensional Polar Harmonic Transforms for Invariant Image Representation,” IEEE Transaction Pattern Analysis and Machine Intelligence, 32(7):1259-1270, 2010.Google Scholar
  74. 74.
    Xiang-yang Wang, Wei-yi Li, Hong-ying Yang, Pei Wang, and Yong-wei Li, “Quaternion polar complex exponential transform for invariant color image description”, Applied Mathematics and Computation, Vol. 256, p. 951–967, 2015.Google Scholar
  75. 75.
    K. M. Hosny and M. M. Darwish, “Accurate computation of quaternion polar complex exponential transform for color images in different coordinate systems,” Journal of Electronic Imaging 26(2), 023021 (2017).CrossRefGoogle Scholar
  76. 76.
    K. M. Hosny and M. M. Darwish, “Highly accurate and numerically stable higher order QPCET moments for color image representation,” Pattern Recognition. Letters, 97, 29–36 (2017)CrossRefGoogle Scholar
  77. 77.
    X. Y. Wang, W. Y. Li, H. Y. Yang, P. P. Niu, Y. W. Li, “Invariant quaternion radial harmonic Fourier moments for color image retrieval”, Optics and Laser Technology, Vol. 66, pp. 78–88, 2015.Google Scholar
  78. 78.
    K. M. Hosny, M. A. Shouman, and H. M. Abdel-Salam, “Fast computation of orthogonal Fourier-Mellin moments in polar coordinates”, Journal of Real-Time Image Process., Vol. 6(2), pp. 73–80, 2011.CrossRefGoogle Scholar
  79. 79.
    K. M. Hosny, M. M. Darwish, “A Kernel-Based method for Fast and accurate computation of PHT in polar coordinates”, Journal of Real-Time Image Process., J Real-Time Image Proc, DOI 10.1007/s11554-016-0622-y, 2016, p.1-13 (Online first).Google Scholar
  80. 80.
    Y. Xin, M. Pawlak, S. Liao, “Accurate computation of Zernike moments in polar coordinates”, IEEE Trans. Image Process., Vol. 16 (2), pp. 581–587, 2007.MathSciNetCrossRefGoogle Scholar
  81. 81.
    J. D. Faires, R. L. Burden, “Numerical Methods”, Brooks Cole Publication, 3rd edn., 2002.Google Scholar
  82. 82.
    C. Camacho-Bello et al., “High precision and fast computation of Jacobi-Fourier moments for image description,” J. Opt. Soc. Am. A 31(1), 124–134 (2014).CrossRefGoogle Scholar
  83. 83.
    C. Camacho-Bello et al., “Reconstruction of color biomedical images by means of quaternion generic Jacobi-Fourier moments in the framework of polar pixel,” J. Med. Imaging 3(1), 014004 (2016).CrossRefGoogle Scholar
  84. 84.
    Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Khalid M. Hosny
    • 1
  • Mohamed M. Darwish
    • 2
  1. 1.Department of Information Technology, Faculty of Computers and InformaticsZagazig UniversityZagazigEgypt
  2. 2.Department of Mathematics, Faculty of ScienceAssiut UniversityAssiutEgypt

Personalised recommendations