Digital Watermarking Based on Joint DWT–DCT and OMP Reconstruction

  • Yifeng ZhangEmail author
  • Yingying Li
  • Yibo Sun


With the rapid development of communication technology and multimedia technology, digital watermark has played an important role in the protection of various forms of digital media works. In this paper, digital watermarking algorithms of wavelet domain are studied. Firstly, we apply discrete wavelet transform with different levels on the host images and combine them with corresponding discrete cosine transform. Next, we introduce the spread transform quantization index modulation algorithm to implement the watermark embedding. Then, we propose orthogonal matching pursuit compression reconstruction algorithm for the watermarking algorithm to optimize the watermark extraction through denoising-attacked watermarked images. We conduct several experiments and compare performance of different algorithms using statistical parameters such as peak signal-to-noise ratio and bit error rate. The experiments and comparisons prove the effectiveness of the proposed algorithms.


Digital watermark Discrete wavelet transform (DWT) Discrete cosine transform (DCT) Spread transform quantization index modulation (STQIM) Orthogonal matching pursuit (OMP) 



  1. 1.
    A. Akter, Nur-E-Tajnina, A.U. Muhammad, Digital image watermarking based on DWT–DCT: evaluate for a new embedding algorithm, in 2014 International Conference on Informatics Electronics and Vision (ICIEV) (2014)Google Scholar
  2. 2.
    O. Benrhouma, O. Mannai, H. Hermassi, Digital images watermarking and partial encryption based on DWT transformation and chaotic maps, in IEEE 12th International Multi-Conference on Systems, Signals and Devices (SSD15) (2015), pp. 1–6Google Scholar
  3. 3.
    E.J. Candès, Compressive sampling, in Proceedings of the International Congress of Mathematicians (2006), pp. 1433–1452Google Scholar
  4. 4.
    B. Chen, G.W. Wornell, Quantization index modulation: a class of provably good methods for digital watermarking and information embedding. IEEE Trans. Inf. Theory 47(4), 1423–1443 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    C. Das, S. Panigrahi, V.K. Sharma, K.K. Mahapatra, A novel blind robust image watermarking in DCT domain using inter-block coefficient correlation. AEU-Int. J. Electron. Commun. 68(3), 244–253 (2014)CrossRefGoogle Scholar
  6. 6.
    K. Deb, M.S. Al-Seraj, M.M. Hoque, M.I.H. Sarkar, Combined DWT–DCT based digital image watermarking technique for copyright protection, in The seventh International Conference on Electrical and Computer Engineering (2012), pp: 458–461Google Scholar
  7. 7.
    D.L. Donoho, Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    M.F.M. El Bireki, M.F.L. Abdullah, A.A.M. Ukasha, A.A. Elrowayati, Digital image watermarking based on joint (DCT–DWT) and arnold transform. Int. J. Secur. Appl. 10(5), 107–118 (2016)Google Scholar
  9. 9.
    R. Gill, R. Soni, Digital image watermarking using 2-DCT and 2-DWT in gray images, in International Conference on Intelligent Computing and Control Systems (ICICCS) (2017), pp. 797–803Google Scholar
  10. 10.
    M.C. Hernandez, M.N. Miyatake, H.M.P. Meana, Analysis of a DFT-based watermarking algorithm, in 2nd International Conference on Electrical and Electronics Engineering (2005), pp. 44–47Google Scholar
  11. 11.
    H.T. Hu, L.Y. Hsu, Collective blind image watermarking in DWT–DCT domain with adaptive embedding strength governed by quality metrics. Multimed. Tools Appl. 76(5), 6575–6594 (2017)CrossRefGoogle Scholar
  12. 12.
    Y.L. Jiang, Y.F. Zhang, W.J. Pei, K. Wang, Adaptive image watermarking algorithm based on improved perceptual models. AEU-Int. J. Electron. Commun. 67(8), 690–696 (2013)CrossRefGoogle Scholar
  13. 13.
    X.B. Kang, F. Zhao, G.F. Lin, Y.J. Chen, A novel hybrid of DCT and SVD in DWT domain for robust and invisible blind image watermarking with optimal embedding strength. Multimed. Tools Appl. 77(11), 13197–13224 (2018)CrossRefGoogle Scholar
  14. 14.
    S.A. Kasmani, A. Naghsh-Nilchi, A new robust digital image watermarking technique based on joint DWT–DCT transformation, in IEEE 3rd International Conference on Convergence and Hybrid Information Technology (2008), pp. 539–544Google Scholar
  15. 15.
    D. Kundur, D. Hatzinakos, Digital watermarking for telltale tamper proofing and authentication. Proc. IEEE 87(7), 1167–1180 (1999)CrossRefGoogle Scholar
  16. 16.
    C.C. Lai, C.C. Tsai, Digital image watermarking using discrete wavelet transform and singular value decomposition. IEEE Trans. Instrum. Meas. 59(11), 3060–3063 (2010)CrossRefGoogle Scholar
  17. 17.
    Q. Li, I.J. Cox, Improved spread transform dither modulation using a perceptual model: robustness to amplitude scaling and JPEG compressing, in IEEE International Conference on Acoustics, Speech and Signal Processing (2007), pp. 185–188Google Scholar
  18. 18.
    M.M. Li, C. Han, A DCT–SVD domain watermarking for color digital image based on compressed sensing theory and chaos theory in 2014 Seventh International Symposium on Computational Intelligence and Design, Hangzhou (2014), pp. 35–38Google Scholar
  19. 19.
    X. Li, J. Liu, J. Sun, X. Yang, W. Liu, Step-projection-based spread transform dither modulation. IET Inf. Secur. 5(3), 170–180 (2011)CrossRefGoogle Scholar
  20. 20.
    S.D. Lin, S.C. Shie, J.Y. Guo, Improving the robustness of DCT-based image watermarking against JPEG compression. Comput. Stand. Interfaces 32(1–2), 54–60 (2010)CrossRefGoogle Scholar
  21. 21.
    H. Liu, D. Xiao, R. Zhang, Y. Zhang, S. Bai, Robust and hierarchical watermarking of encrypted images based on compressive sensing. Signal Process. Image Commun. 45, 41–51 (2016)CrossRefGoogle Scholar
  22. 22.
    K.K. Neetha, A.M. Koya, A compressive sensing approach to DCT watermarking system, in 2015 International Conference on Control Communication and Computing India (ICCC) (2015), pp. 495–500Google Scholar
  23. 23.
    E. Nezhadarya, Z.J. Wang, R.K. Ward, Robust image watermarking based on multiscale gradient direction quantization. IEEE Trans. Inf. Forensic Secur. 6(4), 1200–1213 (2011)CrossRefGoogle Scholar
  24. 24.
    V.M. Potdar, S. Han, E. Chang, A survey of digital image watermarking techniques, in Proceedings of the IEEE International Conference on Industrial Informatics (2005), pp. 709–716Google Scholar
  25. 25.
    D. Singh, S.K. Singh, DWT–SVD and DCT based robust and blind watermarking scheme for copyright protection. Multimed. Tools Appl. 76(11), 13001–13024 (2017)CrossRefGoogle Scholar
  26. 26.
    B. Sukanti, Mardolkar, S. Nayana, A blind digital watermarking algorithm based on DWT–DCT transformation, in Nitte Conference on Advances in Electrical Engineering (2016), pp. 212–216Google Scholar
  27. 27.
    P. Tay, J. Havlicek, Image watermarking using wavelets, in IEEE Midwest Symposium on Circuits and System (2002), pp. 258–261Google Scholar
  28. 28.
    J.A. Tropp, A.C. Gilbert, Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 53(12), 4655–4666 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    T.K. Tsui, X.P. Zhang, D. Androutsos, Color image watermarking using multidimensional fourier transforms. IEEE Trans. Inf. Forensic Secur. 3(1), 16–28 (2008)CrossRefGoogle Scholar
  30. 30.
    W. Wang, A.D. Men, X.B. Chen, Robust image watermarking scheme based on phase features in DFT domain and generalized radon transformations, in 2nd International Congress on Image and Signal Processing (2009), pp. 1510–1514Google Scholar
  31. 31.
    H.C. Xu, X.B. Kang, Y.H. Wang, Y.L. Wang, Exploring robust and blind watermarking approach of colour images in DWT–DCT–SVD domain for copyright protection. Int. J. Electron. Sec. Digit. Forensics 10(1), 79–96 (2018)CrossRefGoogle Scholar
  32. 32.
    Y. Zhang, L.Y. Zhang, J. Zhou, L. Liu, F. Chen, X. He, A review of compressive sensing in information security field. IEEE Access, Special Section Green Commun. Netw. 5G Wirel. 4, 2507–2519 (2016)Google Scholar

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Authors and Affiliations

  1. 1.School of Information Science and EngineeringSoutheast UniversityNanjingPeople’s Republic of China
  2. 2.Nanjing Institute of Communications TechnologiesNanjingPeople’s Republic of China
  3. 3.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingPeople’s Republic of China

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