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Color Watermark Extraction Using Deep Neural Network in IWT Domain with PCA-Based Statistical Feature Reduction

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Abstract

The rapid growth of technology has brought a security threat to digital content over the internet, and therefore, a good watermarking scheme is much needed in the present scenario for image authentication and copyright protection. In this paper, blind color image watermarking using deep neural network (DNN) and statistical features is proposed for providing a better trade-off between imperceptibility and robustness. Integer wavelet transform (IWT) is used to transform the cover image, and PCA has been applied to select the best 10 features out of 18 features. The Watermark image is embedded in the blue color channel of the watermark image. Randomly generated watermark bits are used for creating the training pattern of size 512 × 11, and original watermark bits are used for creating the testing pattern of size 512 × 10. To find the best threshold value, a test on the Lena image was conducted under six image attacks for various threshold values ranges 38–45, and as a result of the test, THR = 42 provided the optimal performance in terms of balancing imperceptibility and robustness. It provides average robustness of 0.9218, 0.9136, 0.9334, and 0.9347 for scaling (0.5), scaling (1.5), salt and pepper noise (0.01), and average filter (3 × 3) image attacks and average imperceptibility of 35.57 dB over ten standard images. The results of the experiment reveal that the suggested watermarking scheme performs better than the state-of-the-art method.

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References

  1. Hadipour A, Afifi R. Advantages and disadvantages of using cryptography in Steganography. In: 17th International ISC Conference on information security and cryptology (ISCISC), 2020; pp. 88–94. https://doi.org/10.1109/ISCISC51277.2020.9261921.

  2. Biermann CJ. Handbook of pulping and papermaking. 2nd ed. San Diego: Academic Press; 1996. (ISBN 0-12-097362-6).

    Google Scholar 

  3. Kumar S, Singh BK, Yadav M. A recent survey on multimedia and database watermarking. Multimed Tools Appl. 2020;79:20149–97.

    Article  Google Scholar 

  4. Khare P, Srivastava VK. A novel dual image watermarking technique using homomorphic transform and DWT. J Intell Syst. 2021;2021(30):297–311. https://doi.org/10.1515/jisys-2019-0046.

    Article  Google Scholar 

  5. Singh D, Singh SK. DWT-SVD and DCT-based robust and blind watermarking scheme for copyright protection. Multimed Tools Appl. 2016;76(11):13001–24.

    Article  Google Scholar 

  6. Anand A, Singh AK. An improved DWT-SVD domain is watermarking for medical information security. Comput Commun. 2020;152:72–80. https://doi.org/10.1016/j.comcom.2020.01.038.

    Article  Google Scholar 

  7. Yasmeen F, Uddin MS. An efficient watermarking approach based on LL and HH edges of DWT-SVD. SN Comput Sci. 2021;2:82. https://doi.org/10.1007/s42979-021-00478-y.

    Article  Google Scholar 

  8. Khare P, Srivastava VK. A reliable and secure image watermarking algorithm using a homomorphic transform in the DWT domain. Multidimens Syst Signal Process. 2021;2021(32):131–60.

    Article  MathSciNet  MATH  Google Scholar 

  9. Liu S, Pan Z, Song H. Digital image watermarking method based on DCT and fractal encoding. IET Image Process. 2017;11:815–21.

    Article  Google Scholar 

  10. Moosazadeh M, Ekbatanifard G. A new DCT-based robust image watermarking method using teaching-learning-based optimization. J Inform Secur Appl. 2019;47:28–38. https://doi.org/10.1016/j.jisa.2019.04.001.

    Article  Google Scholar 

  11. Cedillo-Hernandez M, Cedillo-Hernandez A, Garcia-Ugalde FJ. Improving dft-based image watermarking using particle swarm optimization algorithm. Mathematics. 2021;9(15):1795. https://doi.org/10.3390/math9151795.

    Article  Google Scholar 

  12. Savakar DG, Ghuli A. Non-blind digital watermarking with enhanced image embedding capacity using Meyer wavelet decomposition, SVD, and DFT. Pattern Recognit Image Anal. 2017;27(3):511–7.

    Article  Google Scholar 

  13. Ernawan F, Kabir MN. A block-based RDWT-SVD image watermarking method using human visual system characteristics. Vis Comput. 2020;36(1):19–37.

    Article  Google Scholar 

  14. Thanki R, Kothari A, Borra S. Hybrid, blind and robust image watermarking: RDWT–NSCT based secure approach for telemedicine applications. Multimed Tools Appl. 2021. https://doi.org/10.1007/s11042-021-11064-y.

    Article  Google Scholar 

  15. Verma VS, Jha RK, Ojha A. Digital watermark extraction using support vector machine with principal component analysis-based feature reduction. J Vis Commun Image Represent. 2015;31:75–85.

    Article  Google Scholar 

  16. Islam M, Roy A, Laskar RH. SVM-based robust image watermarking technique in LWT domain using different sub-bands. Neural Comput Appl. 2020;32:1379–403.

    Article  Google Scholar 

  17. Zear A, Singh PK. Secure and robust color image dual watermarking based on LWT-DCT-SVD. Multimed Tools Appl. 2021. https://doi.org/10.1007/s11042-020-10472-w.

    Article  Google Scholar 

  18. Mellimi S, Rajput V, Ansari IA, Ahn CW. A fast and efficient image watermarking scheme based on deep neural network. Pattern Recogn Lett. 2021;151:222–8. https://doi.org/10.1016/j.patrec.2021.08.015.

    Article  Google Scholar 

  19. Mahto DK, Anand A, Singh AK. Hybrid optimization-based robust watermarking using denoising convolutional neural network. Soft Comput. 2022. https://doi.org/10.1007/s00500-022-07155-z.

    Article  Google Scholar 

  20. Mehta R, Gupta K, Yadav AK. An adaptive framework to image watermarking based on the twin support vector regression and genetic algorithm in lifting wavelet transform domain. Multimed Tools Appl. 2020;79:18657–78.

    Article  Google Scholar 

  21. Abdulazeez AM, Hajy DM, Zeebaree DQ, Zebari DA. Robust watermarking scheme based LWT and SVD using artificial bee colony optimization. Indones J Electric Eng Comput Sci. 2021;21(2):1218–29. https://doi.org/10.11591/ideas.v21.i2.pp1218-1229.

    Article  Google Scholar 

  22. Verma VS, Jha RK, Ojha A. Significant region-based robust watermarking scheme in lifting wavelet transform domain. Expert Syst Appl. 2015;42(21):8184–97.

    Article  Google Scholar 

  23. Sharma S, Sharma H, Sharma JB, Poonia RC. A secure and robust color image watermarking using nature-inspired intelligence. Neural Comput Appl. 2020. https://doi.org/10.1007/s00521-020-05634-8.

    Article  Google Scholar 

  24. Chang TJ, Pan IH, Huang PS, Hu CH. A robust DCT-2DLDA watermark for color images. MultimedTools Appl. 2018. https://doi.org/10.1007/s11042-018-6505-4.

    Article  Google Scholar 

  25. Kutter M, Jordan F, Bossen F. Digital watermarking of color images using amplitude modulation. J Electron Imaging. 1998;7(2):326–32.

    Article  Google Scholar 

  26. http://www.imageprocessingplace.com/root_files_V3/image_databases.htm. Accessed 20 Aug 2022.

  27. Abdelhakim AM, Abdelhakim M. A time-efficient optimization for robust image watermarking using machine learning. Expert Syst Appl. 2018;100:197–210.

    Article  Google Scholar 

  28. Golea N, Seghir R, Benzid R. A bind RGB color image watermarking based on singular value decomposition. In: ACS/ IEEE International Conference on computer systems and applications-AICCSA 2010, IEEE, 2020; p. 1–5.

  29. Ansari IA, Pant M, Ahn CW. ABC optimized secured image watermarking scheme to find out the rightful ownership. Optik. 2016;127(14):5711–21.

    Article  Google Scholar 

  30. Islam M, Laskar RH. Geometric distortion correction based robust watermarking scheme in LWT-SVD domain with digital watermark extraction using SVM. Multimed Tools Appl. 2018;77(11):14407–34. https://doi.org/10.1007/s11042-017-5035-9.

    Article  Google Scholar 

  31. Thakkar FN, Srivastava VK. A blind medical image watermarking: DWT-SVD based robust and secure approach for telemedicine applications. Multimed Tools Appl. 2017;76(3):3669–97.

    Article  Google Scholar 

  32. Abdulrahman AK, Ozturk S. A novel hybrid DCT and DWT-based robust watermarking algorithm for color images. Multimed Tools Appl. 2019;78(12):17027–49.

    Article  Google Scholar 

  33. Ansari IA, Pant M. Quality assured and optimized image watermarking using artificial bee colony. Int J Syst Assur Eng Manag. 2018;9(1):274–86.

    Article  Google Scholar 

  34. Swaraja K, Meenakshi K, Kora P. An optimized blind dual medical image watermarking framework for tamper localization and content authentication in secured telemedicine. Biomed Signal Process Control. 2020;55: 101665.

    Article  Google Scholar 

  35. Chen Z, Li L, Peng H, Liu Y, Yang Y. A novel digital watermarking based on general non-negative matrix factorization. IEEE Trans Multimed. 2018;20(8):1973–86.

    Article  Google Scholar 

  36. Kang X, Chen Y, Zhao F, Lin G. Multi-dimensional particle swarm optimization for robust blind image watermarking using intertwining logistic map and hybrid domain. Soft Comput. 2020;24:10561–84. https://doi.org/10.1007/s00500-019-04563-6.

    Article  Google Scholar 

  37. Sharma S, Sharma H, Sharma JB. Artificial bee colony based perceptually tuned blind color image watermarking in hybrid lwtdct domain. Multimed Tools Appl. 2021;80(12):18753–21878.

    Article  MathSciNet  Google Scholar 

  38. Sharma S, Sharma H, Sharma JB. A new optimization-based color image watermarking using non-negative matrix factorization in discrete cosine transform domain. J Ambient Intell Human Comput. 2022;13:4297–319. https://doi.org/10.1007/s12652-021-03408-1.

    Article  Google Scholar 

  39. Hosny KM, Darwish MM. Robust color image watermarking using multiple fractional-order moments and chaotic map. Multimed Tools Appl. 2022;81:24347–75. https://doi.org/10.1007/s11042-022-12282-8.

    Article  Google Scholar 

  40. Ansari IA, Pant M, Ahn CW. Artificial bee colony optimized robust-reversible image watermarking. Multimed Tools Appl. 2017;76(17):18001–25.

    Article  Google Scholar 

  41. Ariatmanto D, Ernawan F. An improved robust image is watermarking by using different embedding strengths. Multimed Tools Appl. 2020;79:12041–67.

    Article  Google Scholar 

  42. Jaiswal S, Pandey MK. Robust digital image watermarking using LWT and Random-Subspace-1DLDA with PCA-based statistical feature reduction. In: 2022 Second International Conference on computer science, engineering and applications (ICCSEA), 2022; p. 1–6.

  43. Jaiswal S, Pandey MK. Deep artificial neural network based blind color image watermarking in YCbCr color domain using statistical features. Int J Comput Eng Res Trends (IJCERT). 2022;10(3):90–8. https://doi.org/10.22362/ijcert/2023/v10/i03/v10i0301. (ISSN: 2349-7084).

    Article  Google Scholar 

  44. Sinhal R, Jain DK, Ansari IA. Machine learning-based blind color image watermarking scheme for copyright protection. Pattern Recogn Lett. 2021;145:171–7. https://doi.org/10.1016/j.patrec.2021.02.011.

    Article  Google Scholar 

  45. Rasouli F, Taheri M. Perfect recovery of small tampers using a novel fragile watermarking technique based on distributed hamming code. The ISC Int J Inform Secur. 2022;14:147–256. https://doi.org/10.22042/ISECURE.2022.284952.670.

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the editor and the anonymous reviewers for their valuable comments and suggestions to improve the quality of this paper. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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  1. Department of CSIT, Guru Ghasidas University, Bilaspur, India

    Sushma Jaiswal & Manoj Kumar Pandey

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  1. Sushma Jaiswal
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This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.

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Jaiswal, S., Pandey, M.K. Color Watermark Extraction Using Deep Neural Network in IWT Domain with PCA-Based Statistical Feature Reduction. SN COMPUT. SCI. 4, 669 (2023). https://doi.org/10.1007/s42979-023-02132-1

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