Cover
Vol. 19 No. 2 (2023)

Published: December 31, 2023

Pages: 18-24

Original Article

Hybrid and Invisible Digital Image Watermarking Technique Using IWT-DCT and Hopfield Neural Network

Ayoub Taheri 1
1IT Engineering Department, Payame Noor University, Tehran, Iran
History
  • Received: January 17, 2023
  • Revised: February 14, 2023
  • Accepted: February 20, 2023
  • Available Online: April 8, 2023
DOI

https://doi.org/10.37917/ijeee.19.2.3

Abstract

According to the characteristic of HVS (Human Visual System) and the association memory ability of neural network, an adaptive image watermarking algorithm based on neural network is proposed invisible image watermarking is secret embedding scheme for hiding of secret image into cover image file and the purpose of invisible watermarking is copyrights protection. Wavelet transformation-based image watermarking techniques provide better robustness for statistical attacks in comparison to Discrete Cosine Transform domain-based image watermarking. The joined method of IWT (Integer Wavelet Transform) and DCT (Discrete Cosine Transform) gives benefits of the two procedures. The IWT have impediment of portion misfortune in embedding which increments mean square estimate as SIM and results diminishing PSNR. The capacity of drawing in is improved by pretreatment and re-treatment of image scrambling and Hopfield neural network. The proposed algorithm presents hybrid integer wavelet transform and discrete cosine transform based watermarking technique to obtain increased imperceptibility and robustness compared to IWT-DCT based watermarking technique. The proposed watermarking technique reduces the fractional loss compared to DWT based watermarking.

Keywords

References

  1. A. Cheddad, J. Condell, K. Curran, and P. Kevitt, “Digi- tal image steganography: Survey and analysis of current methods,” International Journal of Signal Processing, Elsevier, vol. 90, no. 3, pp. 727–752, 2010.
  2. K. Vijay and D. Kumar, “Performance evaluation of dwt based image watermarking,” IEEE 2nd International Advance Computing Conference (IACC), pp. 223–228, 2010.
  3. H. Ahmed, “Hiding color image within a video file using discrete wavelets transform and artificial neural networks,” 2nd International Conference on Electrical, Communication, Computer, Power and Control Engi- neering, pp. 74–79, 2019.
  4. M. Nasrin, B. Khoo, and T. Rassem, “Blockbased dis- crete wavelet transform-singular value decomposition image watermarking scheme using human visual system characteristics,” IET Image Processing, vol. 10, no. 1, pp. 34–52, 2016.
  5. A. M. Joshi, M. Bapna, and M. Meena, “Blind image watermarking of variable block size for copyright pro- tection,” in Proceedings of the International Conference on Recent Cognizance in Wireless Communication & Im- age Processing: ICRCWIP-2014, pp. 853–859, Springer, 2016.
  6. S. Karuna, S. Gupta, U. Kumar, and S. Agrwal, “A new gabor wavelet transform feature extraction technique for ear biometric recognition,” 6th IEEE Power India International Conference (PIICON), pp. 1–3, 2014.
  7. D. Priya, A. Sandeep, K. Gupta, and S. Agrwal, “New gabor-dct feature extraction technique for facial expres- sion recognition,” 5th IEEE International Conference on Communication Systems and Network Technologies (CSNT), pp. 546–549, 2015.
  8. J. Lu, J. Ni, W. Su, and H. Xie, “Wavelet-based cnn for robust and high-capacity image watermarking,” IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6, 2022.
  9. G. Elham, J. Shanbehzadeh, and B. ZahirAzami, “A steganographic method based on integer wavelet trans- form and genetic algorithm,” IEEE International Con- ference on Communications and Signal Processing (ICCSP), pp. 42–45, 2011.
  10. A. Vijaya and V. Kshirsagar, “Robust watermarking scheme based on discrete wavelet transform (dwt) and discrete cosine transform (dct) for copyright protection of digital images,” IJCSNS International Journal of Computer Science and Network Security, vol. 11, no. 8, pp. 208–213, 2011.
  11. G. Ramarao and N. Ramamurthy, “Blind watermarking using bi-orthogonal wavelets, quantization and fuzzy logic,” 2017 International Conference on Intelligent 24 | Taheri Computing and Control Systems (ICICCS), pp. 640–643, 2017.
  12. W. Jasim and R. Mohammed, “A survey on segmenta- tion techniques for image processing,” Iraqi Journal for Electrical and Electronic Engineering, vol. 17, no. 2, pp. 73–93, December 2021.
  13. A. Taheri, “A hybrid digital image watermarking based on discrete wavelet transform, discrete cosine transform and general regression neural network,” IJERSTE, vol. 3, no. 11, pp. 18–28, 2014.
  14. A. Taheri, “Hybrid and adaptive block based watermark- ing algorithm using dwt-dct and artificial neural net- work,” IJERMCA, vol. 4, no. 9, pp. 22–35, Sep-2015.
  15. A. Taheri, “A new robust digital image watermarking based on statistical information of wavelet coefficients transform and back-propagation neural network,” IJER- STE, vol. 3, no. 10, pp. 145–154, 2014.