Advertisement

A Robust Digital Image Watermarking Technique Against Geometrical Attacks Using Support Vector Machine and Glowworm Optimization

  • Parmalik KumarEmail author
  • A. K. Sharma
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 38)

Abstract

The tampering and forgery of digital multimedia data remains as one of the most important challenges all over the internet. Digital image watermarking techniques are used to overcome these challenges. The digital image watermarking techniques provides the security for multimedia data. In this paper, discuss the image features optimization-based digital image watermarking algorithm. The proposed algorithm prevents geometrical attacks during the transmission of data. The proposed algorithm uses support vector machine and glowworm optimization algorithms. The support vector machine classifies the features data of raw image and glowworm optimization used for the reduction cum selection of features for the proceeds of embedded for the extraction of features used SIFT (scale-invariant feature transform) function. The scale-invariant feature transform is major dominated features of critical points and features points. The proposed algorithm implemented in MATLAB software. For the validation and testing purpose used 300 image dataset. For the evaluation of performance measure, these parameters such as PSNR, NC, and SIM. The measured results show better performance instead of SIFT and SVM-GA.

Keywords

Attacks GA GSO SIFT SVM Transform function Watermarking 

References

  1. 1.
    Sun, L., Xu, J., Zhang, X., Tian, Y.: An image watermarking scheme using Arnold transform and fuzzy smooth support vector machine. Math. Probl. Eng. 2015 (2015)Google Scholar
  2. 2.
    Zhou, X., Cao, C., Ma, J., Wang, L.: Adaptive digital watermarking scheme based on support vector machines and optimized genetic algorithm. Math. Probl. Eng. 2018 (2018)Google Scholar
  3. 3.
    Yen, S.H., Wang, C.J.: SVM based watermarking technique. Tamkang J. Sci. Eng. 9(2), 141–150 (2006)MathSciNetGoogle Scholar
  4. 4.
    Kumar, P., Sharma, A.K.: A comprehensive review of digital image watermarking using transform function for the feature extraction and optimization algorithm for dynamic embedding. JETIR. 3(12), 364–379 (2016)Google Scholar
  5. 5.
    Kumar, P., Sharma, A.K.: Analysis of digital watermarking techniques using transform-based function. Int. J. Futur. Revolut. Comput. Sci. Commun. 3, 41–48 (2017)Google Scholar
  6. 6.
    Zhou, X., Zhang, H., Wang, C.: A robust image watermarking technique based on DWT, APDCBT, and SVD. Symmetry 10(3), 77 (2018)CrossRefGoogle Scholar
  7. 7.
    Singh, N., Kumari, J., Aggarwal, C.: Feature selection for steganalysis using glow worm algorithmGoogle Scholar
  8. 8.
    Yepes, V., Martí, J.V., García-Segura, T.: Cost and CO2 emission optimization of precast–prestressed concrete U-beam road bridges by a hybrid glowworm swarm algorithm. Autom. Constr. 1(49), 123–134 (2015)CrossRefGoogle Scholar
  9. 9.
    Marinaki, M., Marinakis, Y.: A glowworm swarm optimization algorithm for the vehicle routing problem with stochastic demands. Expert Syst. Appl. 15(46), 145–163 (2016)CrossRefGoogle Scholar
  10. 10.
    Shrichandran, G.V., Sathiyamoorthy, S., Malarchelvi, P.D.: A hybrid glow-worm swarm optimization with bat algorithm based retinal blood vessel segmentation. J. Comput. Theor. Nanosci. 14(6), 2601–2611 (2017)CrossRefGoogle Scholar
  11. 11.
    Islam, M., Roy, A., Laskar, R.H., et al.: Neural network based robust image watermarking technique in LWT domain. J. Intell. Fuzzy Syst. 34(3), 1691–1700 (2018)CrossRefGoogle Scholar
  12. 12.
    Wang, C., Zhang, Y., Zhou, X.: Robust image watermarking algorithm based on ASIFT against geometric attacks. Appl. Sci. 8(3), 410 (2018)CrossRefGoogle Scholar
  13. 13.
    Nam, S.H., Kim, W.H., Mun, S.M., Hou, J.U., Choi, S., Lee, H.K.: A SIFT features based blind watermarking for DIBR 3D images. Multimed. Tools Appl. 77(7), 7811–7850 (2018)CrossRefGoogle Scholar
  14. 14.
    Singh, K.M.: A robust rotation resilient video watermarking scheme based on the SIFT. Multimed. Tools Appl. 77(13), 16419–16444 (2018)CrossRefGoogle Scholar
  15. 15.
    Li, J., Lin, Q., Yu, C., Ren, X., Li, P.: A QDCT-and SVD-based color image watermarking scheme using an optimized encrypted binary computer-generated hologram. Soft. Comput. 22(1), 47–65 (2018)CrossRefGoogle Scholar
  16. 16.
    Zhou, N.R., Luo, A.W., Zou, W.P., et al.: Secure and robust watermark scheme based on multiple transforms and particle swarm optimization algorithm. Multimed. Tools Appl. 78(2), 2507–7523 (2019)CrossRefGoogle Scholar
  17. 17.
    Zheng, Z., Saxena, N., Mishra, K.K., Sangaiah, A.K., et al.: Guided dynamic particle swarm optimization for optimizing digital image watermarking in industry applications. Futur. Gener. Comput. Syst. 88, 92–106 (2018)CrossRefGoogle Scholar
  18. 18.
    Ali, M., Ahn, C.W., et al.: An optimal image watermarking approach through cuckoo search algorithm in wavelet domain. Int. J. Syst. Assur. Eng. Manag. 9(3), 602–611 (2018)CrossRefGoogle Scholar
  19. 19.
    Ansari, I.A., Pant, M., Ahn, C.W., et al.: Artificial bee colony optimized robust-reversible image watermarking. Multimed. Tools Appl. 76(17), 18001–18025 (2017)CrossRefGoogle Scholar
  20. 20.
    Bahrami, Z., Tab, F.A., et al.: A new robust video watermarking algorithm based on SURF features and block classification. Multimed. Tools Appl. 77(1), 327–345 (2018)CrossRefGoogle Scholar
  21. 21.
    Balasamy, K., Ramakrishnan, S., et al.: An intelligent reversible watermarking system for authenticating medical images using wavelet and PSO. Cluster Comput. 1–2 (2018)Google Scholar
  22. 22.
    Chatterjee, S., Sarkar, S., Hore, S., Dey, N., Ashour, A.S., Balas, V.E., et al.: Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Comput. Appl. 28(8), 2005–2016 (2017)CrossRefGoogle Scholar
  23. 23.
    Li, D., Deng, L., Gupta, B.B., Wang, H., Choi, C., et al.: A novel CNN based security guaranteed image watermarking generation scenario for smart city applications. Inf. Sci. 479, 432–447 (2019)CrossRefGoogle Scholar
  24. 24.
    Makbol, N.M., Khoo, B.E., Rassem, T.H., Loukhaoukha, K., et al.: 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 (2017)CrossRefGoogle Scholar
  25. 25.
    Mehta, R., Rajpal, N., Vishwakarma, V.P., et al.: A robust and efficient image watermarking scheme based on Lagrangian SVR and lifting wavelet transform. Int. J. Mach. Learn. Cybern. 8(2), 379–395 (2017)CrossRefGoogle Scholar
  26. 26.
    Mitashe, M.R., Habib, A.R., Razzaque, A., Tanima, I.A., Uddin, J., et al.: An adaptive digital image watermarking scheme with PSO, DWT and XFCM. In: 2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 13 February 2017, pp. 1–5. IEEE (2017)Google Scholar
  27. 27.
    Saxena, N., Mishra, K.K., et al.: Improved multi-objective particle swarm optimization algorithm for optimizing watermark strength in color image watermarking. Appl. Intell. 47(2), 362–381 (2017)CrossRefGoogle Scholar
  28. 28.
    Shih, F.Y., Zhong, X., Chang, I.C., Satoh, S.I., et al.: An adjustable-purpose image watermarking technique by particle swarm optimization. Multimed. Tools Appl. 77(2), 1623–1642 (2018)CrossRefGoogle Scholar
  29. 29.
    Sun, L., Xu, J., Liu, S., Zhang, S., Li, Y., Shen, C.A., et al.: A robust image watermarking scheme using Arnold transform and BP neural network. Neural Comput. Appl. 30(8), 2425–2440 (2018)CrossRefGoogle Scholar
  30. 30.
    Thakkar, F.N., Srivastava, V.K., et al.: A fast watermarking algorithm with enhanced security using compressive sensing and principle components and its performance analysis against a set of standard attacks. Multimed. Tools Appl. 76(14), 15191–15219 (2017)CrossRefGoogle Scholar
  31. 31.
    Kukenys, I., McCane, B., et al.: Classifier cascades for support vector machines, pp. 1–6. IEEE (2008)Google Scholar
  32. 32.
    Camlica, Z., Tizhoosh, H.R., Khalvati, F., et al.: Medical image classification via SVM using LBP features from saliency-based folded data, pp. 1–5. IEEE (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Shri Venkateshwara UniversityGajraulaIndia

Personalised recommendations