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LWT-DCT-SVD and DWT-DCT-SVD based watermarking schemes with their performance enhancement using Jaya and Particle swarm optimization and comparison of results under various attacks

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Abstract

Robust and undetectable watermarking scheme is important for copyright protection. This paper proposes a DWT-DCT-SVD and LWT-DCT-SVD based image watermarking technology, as well as a comparison of the DWT and LWT systems. The host picture is first divided into sub-bands throughout the embedding phase using multilevel DWT or LWT. The image is divided into various high and low-frequency sub-bands using DWT. LWT breaks up the original filters into a series of smaller structures and provides a faster algorithm. The ensuing sub-bands area unit is then used as the input for DCT. The SVD is performed after applying DCT. The watermark is then inserted into the input image sub-band by using particle swarm optimization intelligence and JAYA optimization to discover an acceptable scale factor. The proposed methodology is compared with other methods under a variety of first-generation and second-generation attacks, including filtering, noise, JPEG compression, JPEG2000 compression, sharpening, thresholding, dithering, motion blur, and shear attack. Furthermore, the proposed watermarking system has a reasonable level of robustness and imperceptibility in the face of most attacks, therefore it is a suitable technique for Copywrite protection. This paper proposes two watermarking schemes based on discrete wavelet transform (DWT) namely DWT-DCT-SVD and lifting wavelet transform (LWT) based scheme namely LWT-DCT-SVD. The comparison of these two schemes is also carried out.

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References

  1. Allaf AH, Kbir MA (2018). A review of digital watermarking applications for medical image exchange security. In the proceedings of the third international conference on smart city applications, pp 472–480

  2. Arya RK, Singh S, Saharan R (2015). A secure non-blind block-based digital image watermarking technique using DWT and DCT. In: IEEE international conference on advances in computing, communications and informatics (ICACCI), pp 2042–2048

  3. Ayangar VR and Talbar SN (2010). A novel DWT-SVD based watermarking scheme. International conference on multimedia computing and information technology (MCIT), Sharjah, pp. 105–108. https://doi.org/10.1109/MCIT.2010.5444871.

  4. Chandra DVS (n.d.) (2002) digital image watermarking using singular value decomposition. The 2002 45th Midwest symposium on circuits and systems, 2002. MWSCAS-2002. https://doi.org/10.1109/mwscas.2002.1187023

  5. Fowler JE (2005) The redundant discrete wavelet transform and additive noise. IEEE Signal Processing Letters 12(9):629–632. https://doi.org/10.1109/lsp.2005.853048

    Article  Google Scholar 

  6. Furqan A, Kumar M (2015). Study and analysis of robust DWT-SVD domain-based digital image watermarking technique using MATLAB. IEEE International Conference on Computational Intelligence & Communication Technology.

  7. Guo J-M, Prasetyo H (2014) False-positive-free SVD-based image watermarking. J Vis Commun Image Represent 25(5):1149–1163. https://doi.org/10.1016/j.jvcir.2014.03.012

    Article  Google Scholar 

  8. Hafed ZM, Levine M (2004) Face recognition using the discrete cosine transform. Int J Comput Vis 43:167–188

    Article  Google Scholar 

  9. Hema Rajini N (2019) Digital image watermarking using optimization and encryption. J Phys Conf Ser 1362:012090. https://doi.org/10.1088/1742-6596/1362/1/012090

    Article  Google Scholar 

  10. Hurrah N, Parah S, Loan N, Sheikh J, Elhoseny M, Muhammad K (2018) Dual watermarking framework for privacy protection and content authentication of multimedia. Futur Gener Comput Syst 94:654–673. https://doi.org/10.1016/j.future.2018.12.036

    Article  Google Scholar 

  11. Jane O, Elbaşi E, İlk HG (2014) Hybrid non-blind watermarking based on DWT and SVD. Journal of Applied Research and Technology 12(4):750–761. https://doi.org/10.1016/s1665-6423(14)70091-4

    Article  Google Scholar 

  12. Kaur J, Singh N and Jain C (2016). An improved image watermarking technique implementing 2-DWT and SVD. IEEE international conference on recent trends in electronics, Information & Communication Technology (RTEICT), Bangalore, 2016, pp. 1855–1859. https://doi.org/10.1109/RTEICT.2016.7808156.

  13. Kumar S, Singh BK, Yadav M (2020) A recent survey on multimedia and database watermarking. Multimed Tools Appl 79:20149–20197. https://doi.org/10.1007/s11042-020-08881-y

    Article  Google Scholar 

  14. Liao X, Li K, Zhu X and Liu KJR (2020). Robust Detection of Image Operator Chain with Two-Stream Convolutional Neural Network. In IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 5, pp. 955–968, Aug. 2020. https://doi.org/10.1109/JSTSP.2020.3002391.

  15. Liao X, Yu Y, Li B, Li Z and Qin Z (2020). A new payload partition strategy in color image steganography. In IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 3, pp. 685–696, March 2020. https://doi.org/10.1109/TCSVT.2019.2896270.

  16. Liao X, Yin J, Chen M, Qin Z (2020). Adaptive payload distribution in multiple images steganography based on image texture features. IEEE transactions on dependable and secure computing, 1–1. https://doi.org/10.1109/tdsc.2020.3004708

  17. Liu J, Huang J, Luo Y, Cao L, Yang S, Wei D, Zhou R (2019) An optimized image watermarking method based on HD and SVD in DWT domain. IEEE Access 1–1:2019–80860. https://doi.org/10.1109/access.2019.2915596

    Article  Google Scholar 

  18. Maity A, Pattanaik A, Sagnika S, Pani S (2015) A comparative study on approaches to speckle noise reduction in images. International Conference on Computational Intelligence and Networks. https://doi.org/10.1109/cine.2015.36

  19. Makbol NM, Khoo BE (2013) A hybrid robust image watermarking scheme using integer wavelet transform. Singular Value Decomposition and Arnold Transform Lecture Notes in Computer Science:36–47. https://doi.org/10.1007/978-3-319-02958-0_4

  20. Naik NS, Naveena N and Manikantan K (2015). Robust digital image watermarking using DWT+SVD approach. IEEE international conference on computational intelligence and computing research (ICCIC), Madurai, 2015, pp. 1–6. https://doi.org/10.1109/ICCIC.2015.7435653.

  21. Nandi S, Santhi V (2016) DWT–SVD-based watermarking scheme using optimization technique. In: Dash S, Bhaskar M, Panigrahi B, Das S (eds) Artificial intelligence and evolutionary computations in engineering systems, advances in intelligent systems and computing, vol 394. Springer, New Delhi, pp 69–77

    Google Scholar 

  22. Navas KA, Ajay MC, Lekshmi M, Archana TS, Sasikumar M. (2008) DWT DCT SVD Based Watermarking. 3rd International Conference on Communication Systems Software and Middleware and Workshops, COMSWARE 2008.

  23. Rastegar S, Namazi F, Yaghmaie K, Aliabadian A (2011) Hybrid watermarking algorithm based on singular value decomposition and radon transform. AEU - International Journal of Electronics and Communications 65(7):658–663. https://doi.org/10.1016/j.aeue.2010.09.008

    Article  Google Scholar 

  24. Rykaczewski R (2007) Comments on “An SVD-Based Watermarking Scheme for Protecting Rightful Ownership.”. IEEE Transactions on Multimedia 9(2):421–423. https://doi.org/10.1109/tmm.2006.886297

    Article  Google Scholar 

  25. Singh AK, Dave M, Mohan A (2014) Hybrid technique for robust and imperceptible image watermarking in DWT–DCT–SVD domain. Natl Acad Sci Lett 37(4):351–358

    Article  Google Scholar 

  26. Thakkar F, Srivastava V (2017). A particle swarm optimization and block-SVD-based watermarking for digital images. TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES. 25. 3273–3288. https://doi.org/10.3906/elk-1603-17.

  27. Vaish A, Kumar M (2017) Color image encryption using MSVD, DWT and Arnold transform in fractional Fourier domain. Optik 145:273–283. https://doi.org/10.1016/j.ijleo.2017.07.041

    Article  Google Scholar 

  28. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612. https://doi.org/10.1109/tip.2003.819861

    Article  Google Scholar 

  29. Wang B, Ding J, Wen Q, Liao X and Liu C (2009). An image watermarking algorithm based on DWT DCT and SVD. IEEE international conference on network infrastructure and digital content, pp. 1034–1038. https://doi.org/10.1109/ICNIDC.2009.5360866.

  30. Zear A, Singh AK, Kumar P (2018) A proposed secure multiple watermarking technique based on DWT, DCT and SVD for application in medicine. Multimed Tools Appl 77:4863–4882

    Article  Google Scholar 

  31. Zeki AM, Manaf AA, Mahmod SS (2011). Analysis of ISB watermarking model: block-based methods vs embedding repetition methods. In proceedings of the 9th international conference on advances in Mobile computing and multimedia (MoMM '11). Association for Computing Machinery, New York, NY, USA, 198–201. https://doi.org/10.1145/2095697.2095734

  32. Zhang X-P, Li K (2005) Comments on “An SVD-based watermarking scheme for protecting rightful Ownership.”. IEEE Transactions on Multimedia 7(3):593–594. https://doi.org/10.1109/tmm.2005.843357

    Article  Google Scholar 

  33. Zhang L, Wei (2019) D. Dual DCT-DWT-SVD digital watermarking algorithm based on particle swarm optimization. Multimedia Tools Appl 78:28003–28023. https://doi.org/10.1007/s11042-019-07902-9

    Article  Google Scholar 

  34. Zhang H, Wang C, Zhou X (2017) A robust image watermarking scheme based on SVD in the spatial domain. Future Internet 9(3):45. https://doi.org/10.3390/fi9030045

    Article  Google Scholar 

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  1. Department of Electronics and Communication Engineering, MNNIT, Allahabad, India

    Divyanshu Awasthi & Vinay Kumar Srivastava

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  1. Divyanshu Awasthi
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  2. Vinay Kumar Srivastava
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Correspondence to Divyanshu Awasthi.

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Awasthi, D., Srivastava, V.K. LWT-DCT-SVD and DWT-DCT-SVD based watermarking schemes with their performance enhancement using Jaya and Particle swarm optimization and comparison of results under various attacks. Multimed Tools Appl 81, 25075–25099 (2022). https://doi.org/10.1007/s11042-022-12456-4

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