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Color image watermarking based on singular value decomposition and generalized regression neural network

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Abstract

In this paper, a novel singular value decomposition (SVD) based color image watermarking scheme is proposed. Each color image block is processed by converting it into the two dimensional (2D) matrix followed by SVD. Then watermark is embedded into the first singular value of each 2D matrix. The original singular value information is generalized by taking advantage of nonlinear map ability of generalized regression neural network (GRNN). Finally, watermark can be recovered using the modified singular value and the generalized neural network model. Without directly providing the original singular value information at the extraction stage, the proposed algorithm can reduce the risk of cracking original image. Moreover, watermark invisibility and robustness of the proposed watermarking scheme is achieved by adaptively selecting the embedding strength of each image block. Experimental results show that the proposed algorithm is reliable and robust to most common attacks, such as filtering, JPEG compression.

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References

  1. Al-Nuaimy W, El-Bendary MAM, Shafik A, Shawki F, Abou-El-azm AE, El-Fishawy NA, Elhalafawy SM, Elhalafawy SM, Diab SM, Sallam BM, El-Samie FEA, Kazemian HB (2011) An SVD audio watermarking approach using chaotic encrypted images. Digit Signal Process 21(6):764–779

    Article  Google Scholar 

  2. Bas PP, Bihan NL, Chassery JM (2003) Color image watermarking using quaternion fourier transform. IEEE International Conference on Acoustics, Speech, and Signal Processing. 3(ICASSP '03): 521–524.

  3. Chen B, Zhou C, Jeon B, Zheng Y, Wang J (2018) Quaternion discrete fractional random transform for color image adaptive watermarking. Multimedia Tools Appl 77:20809–20837

    Article  Google Scholar 

  4. Chen BJ, Coatrieux G, Chen G, Sun XM, Coatrieux JL, Shu HZ (2014) Full 4-D quaternion discrete Fourier transform based watermarking for color images. Digit Signal Process 28(5):106–119

    Article  Google Scholar 

  5. Cigizoglu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37(2):63–68

    Article  Google Scholar 

  6. Faragallah O (2013) Efficient video watermarking based on singular value decomposition in the discrete wavelet transform domain. AEU-Int. J. Electron. Commun. 67(3):189–119

    Article  Google Scholar 

  7. Fares K, Amine K, Salah E (2020) A robust blind color image watermarking based on Fourier transform domain. Optik 208:164562–1–164562–9

    Article  Google Scholar 

  8. Fernández-Gámez MA, Gil-Corral AM, Valdivieso FG (2016) Corporate reputation and market value: evidence with generalized regression neural networks. Expert Syst Appl 46(15):69–76

    Article  Google Scholar 

  9. Ganic E, Eskicioglu AM (2005) Robust embedding of visual watermarks using discrete wavelet transform and singular value decomposition. J Eletron Imaging 14(4):043004

    Article  Google Scholar 

  10. Ghazy RA, Abbas AM, Al-Zubi N, Hassan ES, El-Fishawy NA, Hadhoud MM, Dessouky MI, El-Rabaie EM, Alshebeili SA, El-Samie FEA (2015) Block-based SVD image watermarking in spatial and transform domains. Int J Electron 102(7):1091–1113

    Article  Google Scholar 

  11. Goulermas JY, Liatsis P, Zeng XJ, Cook P (2007) Density-driven generalized regression neural networks (DD-GRNN) for function approximation. IEEE Trans Nerual Netw 18(6):1683–1696

    Article  Google Scholar 

  12. Guo J, Prasetyo H (2014) False-positive-free SVD-based image watermarking. J Vis Commun Image Represent 25(5):1149–1163

    Article  Google Scholar 

  13. Hartung F, Kutter M (1999) Multimedia watermarking techniques. Proceed IEEE 87(7):1079–1107

    Article  Google Scholar 

  14. Hou R, Hu Y, Zhao YH, Liu H (2020) Hyperspectral image quality evaluation using generalized regression neural network. Signal Process-Image Commun 83:115785

    Article  Google Scholar 

  15. Hu R, Wen S, Zeng Z, Huang T (2017) A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing 221:24–31

    Article  Google Scholar 

  16. Hu T, Hsu L, Chou H (2020) An improved SVD-based blind color image watermarking algorithm with mixed modulation incorporated. Inf Sci 519:161–182

    Article  MathSciNet  Google Scholar 

  17. Hua K, Dai B, Srinivasan K, Hsu Y, Sharma V (2017) A hybrid NSCT domain image watermarking scheme. EURASIP J Image Vide 2017(10):1–17

    Google Scholar 

  18. Huang H, Chen D, Lin C, Chen S, Hsu W (2015) Improving SVD-based image watermarking via block-by-block optimization on singular values. EURASIP J Image Vide 2015(25):1–10

    Google Scholar 

  19. Jiao S, Zhou C, Shi Y, Zou W, Li X (2019) Review on optical image hiding and watermarking techniques. Opt Laser Technol 109:370–380

    Article  Google Scholar 

  20. Khosravi MR, Samadi S (2019) Efficient payload communications for IoT-enabled ViSAR vehicles using discrete cosine transform-based quasi-sparse bit injection. EURASIP J Wirel Commun Netw 2019(1):262

    Article  Google Scholar 

  21. Khosravi MR, Samadi S (2020) Reliable data aggregation in internet of ViSAR vehicles using chained dual-phase adaptive interpolation and data embedding. IEEE Internet Things J 7(4):2603–2610

    Article  Google Scholar 

  22. Lai C, Tsai C (2010) Digital image watermarking using discrete wavelet transform and singular value decomposition. IEEE Trans Instrum Meas 59(11):3060–3063

    Article  Google Scholar 

  23. Lei B, Ni D, Chen S, Wang T, Zhou F (2014) Optimal image watermarking scheme based on chaotic map and quaternion wavelet transform. Nonlinear Dyn 78(4):2897–2907

    Article  Google Scholar 

  24. Li B, Ding J, Yin Z, Li K, Zhao X, Zhang L (2021) Optimized neural network combined model based on the induced ordered weighted averaging operator for vegetable price forecasting. Expert Syst Appl 168:114232

    Article  Google Scholar 

  25. Liu R, Tan T (2002) An SVD-based watermarking scheme for protecting rightful ownership. IEEE Trans Multimed 4(1):121–128

    Article  Google Scholar 

  26. Loukhaoukha K, Refaey A, Zebbiche K (2016) Comment on “a robust color image watermarking with singular value decomposition method”. Adv Eng Softw 93:44–46

    Article  Google Scholar 

  27. Makbol N, Khoo B (2013) Robust blind image watermarking scheme based on redundant discrete wavelet transform and singular value decomposition. AEU-Int J Electron Commun 67(2):102–112

    Article  Google Scholar 

  28. Mohammad AA, Alhaj A, Shaltaf S (2008) An improved SVD-based watermarking scheme for protecting rightful ownership. Signal Process 88(9):2158–2180

    Article  Google Scholar 

  29. Pei SC, Liu HH (2008) Improved SVD-based watermarking for digital images. In: 2008 Sixth Indian Conference on Computer Vision, Graphics&Image Processing. 273–280.

  30. Pei SC, Liu HH, Liu TJ, Liu KH (2010) Color image watermarking using SVD, in: 2010 IEEE Int. Conf. on Multimedia and Expo. 122–126.

  31. Run R, Horng S, Lai J, Kao T, Chen R (2012) An improved SVD-based watermarking technique for copyright protection. Expert Syst Appl 39(1):673–689

    Article  Google Scholar 

  32. Savelonas MA, Chountasis S (2010) Noise-resistant watermarking in the fractional Fourier domain utilizing moment-based image representation. Signal Process 90(8):2521–2528

    Article  Google Scholar 

  33. Specht DF (1991) A general regression neural network. IEEE Trans Neural Netw 2(6):568–576

    Article  Google Scholar 

  34. Su Q, Chen B (2017) A novel blind color image watermarking using upper Hessenberg matrix. AEU-Int J Electron Commun 78:64–71

    Article  Google Scholar 

  35. Sun L, Xu J, Liu S, Zhang S, Li Y, Shen C (2018) A robust image watermarking scheme using Arnold transform and neural network. Neural Comput & Applic 30:2425–2440

    Article  Google Scholar 

  36. Sun Y, Su Q, Wang H, Wang G (2022) A blind dual color images watermarking based on quaternion singular value decomposition. Multimed Tools Appl 81:6091–6113

    Article  Google Scholar 

  37. The MPEG-7 database <http://www.dabi.temple.edu/~shape/MPEG7/dataset.html>

  38. The USC-SIPI image database <http://sipi.usc.edu/database/>

  39. Tsougenis E, Papakostas G, Koulouriotis D, Tourassis V (2012) Performance evaluation of moment-based watermarking methods: a review. J Syst Softw 85(8):1864–1884

    Article  Google Scholar 

  40. Urvoy M, Goudia D, Autrusseau F (2014) Perceptual DFT watermarking with improved detection and robustness to geometrical distortions. IEEE Trans Inf Forensics Secur 9(7):1108–1119

    Article  Google Scholar 

  41. Wang F, Pan J (2009) SpringerLink (online service), innovations in digital watermarking techniques. Springer, Berlin Heidelberg

  42. Wang X, Niu P, Yang H, Wang C, Wang A (2014) A new robust color image watermarking using local quaternion exponent moments. Inf Sci 277:731–754

    Article  Google Scholar 

  43. Wu Y (2005) On the security of an SVD-based ownership watermarking. IEEE Trans Multimed 7(4):624–627

    Article  Google Scholar 

  44. Yavuz E, Telatar Z (2007) Improved SVD-DWT based digital image watermarking against watermark ambiguity, in: Proceedings of the 2007 ACM symposium on Applied computing. 1051–1055.

  45. Zhang X, Li K (2005) Comments on "an SVD-based watermarking scheme for protecting rightful ownership". IEEE Trans Multimed 7(2):593–594

    Article  Google Scholar 

  46. Zhu T, Qu W, Cao WL (2021) An optimized image watermarking algorithm based on SVD and IWT. J Supercomput 78:222–237. https://doi.org/10.1007/s11227-021-03886-2

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 61901292, Project of Beijing Excellent Talents (No.2016000020124G088), Beijing Municipal Education Research Plan Project (SQKM201810028018), Project supported by Fundamental Research Program of Shanxi Province, China (202103021224057), the Natural Science Foundation of Shanxi Province, China (Nos. 201801D221186, and 201901D211080), Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (No. 2019 L0145), and School Foundation of Taiyuan University of Technology (Nos. 2017QN11 and 2017QN12).

Declarations

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of this work.

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Authors and Affiliations

  1. College of Data Science, Taiyuan University of Technology, Shanxi, China

    Xilin Liu, Yongfei Wu & Peiting Gao

  2. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China

    Junlin Ouyang

  3. College of Information Engineering, Capital Normal University, Beijing, China

    Zhuhong Shao

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  1. Xilin Liu
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  2. Yongfei Wu
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Correspondence to Xilin Liu.

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Liu, X., Wu, Y., Gao, P. et al. Color image watermarking based on singular value decomposition and generalized regression neural network. Multimed Tools Appl 81, 32073–32091 (2022). https://doi.org/10.1007/s11042-022-12990-1

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