Robust image watermarking using fractional Krawtchouk transform with optimization

Abstract

Fractional Krawtchouk Transform is a generalization of the Krawtchouk transforms which has two fractional orders. By adjusting these fractional orders in the weighted two dimensional Krawtchouk polynomials, local image features can be located. This paper proposes a robust image watermarking method using FrKT with firefly and cuckoo search optimization algorithms. The frequency domain image is obtained by applying FrKT for the input image blocks. The optimal fractional parameters of the transform improve the imperceptibility of the secret data in the host images. The fractional parameter selection for the image transformation is performed by Firefly optimization algorithm. Also, the optimal location in each block to hide the secret data is identified by the cuckoo search algorithm. The histogram shifting technique is used to embed the secret data in the optimal locations due to its less computational complexity. The parameters like Peak Signal to Noise Ratio, Normalized Correlation Coefficient, Structural SIMilarity index, Bit Error Rate are used for comparison of the proposed method using the optimization algorithms. The experimental results of the proposed method FrKT with combination of both Firefly and cuckoo search optimization shows better quality of the watermark, robustness and imperceptibility against various attacks. It can be concluded that the proposed FrKT + CS + FA provides an average of 0.92 for most of the attacks that prove the robustness of the proposed scheme.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3

References

  1. Al-Haj A, Abu-Errub A (2008) Performance optimization of discrete wavelets transform based image watermarking using genetic algorithms. J Comput Sci 1:834–841

    Google Scholar 

  2. Ali M, Ahn CW (2015) Comments on “Optimized gray-scale image watermarking using DWT-SVD and Firefly Algorithm”. Expert Syst Appl 42(5):2392–2394

    Google Scholar 

  3. Ansari IA, Pant M (2017) Multipurpose image watermarking in the domain of DWT based on SVD and ABC. Pattern Recogn Lett 94:228–236

    Google Scholar 

  4. Ansari IA, Pant M, Ahn CW (2017) Artificial bee colony optimized robust-reversible image watermarking. Multimedia Tools Appl 76(17):18001–18025

    Google Scholar 

  5. Atakishiyev NM, Wolf KB (1997) Fractional Fourier–Kravchuk transform. J Opt Soc Am 14(7):1467–1477

    MathSciNet  Google Scholar 

  6. Barni M, Bartolini F, Piva A (2001) Improved wavelet-based watermarking through pixel-wise masking. IEEE Trans Image Process 10(5):783–791

    MATH  Google Scholar 

  7. Cedillo-Hernández M, García-Ugalde F, Nakano-Miyatake M, Pérez-Meana HM (2014) Robust hybrid color image watermarking method based on DFT domain and 2D histogram modification. J Signal Image Video Process 8(01):49–63

    Google Scholar 

  8. Chau L, Siu W (2013) Efficient multiplier structure for realization of the discrete co-sine transform. J Signal Process Image Commun 18:527–536

    Google Scholar 

  9. Dong P, Brankov JB, Galatsanos NP, Yang Y, Davoine F (2005) Digital watermarking robust to geometric distortions. IEEE Trans Image Process 14(12):2140–2150

    Google Scholar 

  10. Friedberg SH, Insel AJ, Spence LE (1979) Linear Algebra Englewood Cliffs. Prentice-Hall, NJ

    Google Scholar 

  11. Hsu C, Wu J (1999) Hidden digital watermarks in images. IEEE Trans Image Process 8(01):58–68

    Google Scholar 

  12. Li J, Zhu Y (2010) A geometric robust image watermarking scheme based on DWT-SVD and Zernike moments. In: Proceedings of 3rd IEEE international conference on computer science and information technology (ICCSIT), pp 367–371

  13. Li J, Zhu Y (2010) A geometric robust image watermarking scheme based on DWT-SVD and Zernike moments. In: Proceeding 3rd IEEE International conference on computer science and information technology (ICCSIT), pp 367–371

  14. Li Q, Yuan C, Zong YZ (2007) Adaptive DWT–SVD domain image watermarking using human visual model. In: ICACT, pp 1947–1951

  15. Liang J, Tran TD (2001) Fast multiplier less approximations of the DCT with the lifting scheme. IEEE Trans Signal Process 49(12):3032–3044

    Google Scholar 

  16. Liu F, Liu Y (2008) A watermarking algorithm for digital image based on DCT and SVD. IEEE Congress Image Signal Process 1:380–383

    Google Scholar 

  17. Makbol NM, Khoo BE, Taha HR, Loukhaoukha K (2017) A new reliable optimized image watermarking scheme based on the integer wavelet transform and singular value decomposition for copyright protection. Inf Sci 417:381–400

    Google Scholar 

  18. Mishra A, Agarwal C, Sharma A, Bedi P (2014) Optimized gray-scale image watermarking using DWT-SVD and Firefly Algorithm. Expert Syst Appl 41(17):7858–7867

    Google Scholar 

  19. Mohanarathinam A, Kamalraj S, Prasanna Venkatesan GKD et al (2019) Digital watermarking techniques for image security: a review. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01500-1

    Article  Google Scholar 

  20. Najafi E, Loukhaoukha K (2019) Hybrid secure and robust image watermarking scheme based on SVD and sharp frequency localized contourlet transform. J Inf Secur Appl 44:144–156

    Google Scholar 

  21. Papakostas GA, Tsougenis ED, Koulouriotis E (2010) Near optimum local image watermarking using Krawtchouk moments. In: Proceedings of IEEE international workshop imaging system technology, pp 464–467

  22. Sejdic E, Djurovic I, Stankovic L (2011) Fractional Fourier transforms a signal processing tool: an overview of recent developments. Signal Process 91(6):1351–1369

    MATH  Google Scholar 

  23. Singh SP, Bhatnagar G (2020) A robust blind watermarking framework based on Dn structure. J Ambient Intell Human Comput 11:1869–1887. https://doi.org/10.1007/s12652-019-01296-0

    Article  Google Scholar 

  24. Singh C, Ranade SK (2013) Geometrically invariant and high capacity image water-marking scheme using accurate radial transform. Opt Laser Technol 54(30):176–184

    Google Scholar 

  25. Sinsinwar K, Chauhan SPS (2016) A survey of digital image watermarking optimization algorithms inspired by nature. Int J Sci Res 5(2):1593–1599

    Google Scholar 

  26. Sundararajana M, Yamuna G (2018) Optimization of colour image watermarking using area of best fit 3equation and cuckoo search algorithm. Mater Today Proc 5:1138–1146

    Google Scholar 

  27. Surekha P, Sumathi S (2011) Implementation of genetic algorithm for a DWT based image watermarking scheme. ICTACT J Soft Comput 02(01):244–252

    Google Scholar 

  28. Tamirat TT, Rajesh Kumar P, Lavanya DG (2017) Robust image watermarking scheme using population-based stochastic optimization technique. Int J Image Gr Signal Process 7:55–65

    Google Scholar 

  29. Tao R, Deng B, Wang Y (2009) Fractional Fourier transform and its applications. Tisinghua Univ. Press, Beijing

    Google Scholar 

  30. Verma VS, Jha RK (2015) An overview of robust digital image watermarking. IETE Tech Rev 32(6):479–496

    Google Scholar 

  31. Xin Y, Liao S, Pawlak M (2007) Circularly orthogonal moments for geo-metrically robust image watermarking. Pattern Recogn Lett 40(12):3740–3752

    MATH  Google Scholar 

  32. Yap P, Paramesran R, Ong S (2003) Image analysis by Krawtchouk moments. IEEE Trans Image Process 12(11):1367–1377

    MathSciNet  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Rajkumar Ramasamy.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ramasamy, R., Arumugam, V. Robust image watermarking using fractional Krawtchouk transform with optimization. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02379-z

Download citation

Keywords

  • FrKT
  • Histogram shifting
  • Firefly Algorithm
  • Cuckoo Search Algorithm
  • Optimization