Skip to main content
SpringerLink
Log in

A robust image watermarking scheme using Arnold transform and BP neural network

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The protection and authentication of multimedia contents and copyright have become a great concern in the fast-growing Internet environment. This paper presents an optimized robust watermarking scheme based on Arnold transform and back propagation (BP) neural network in compressed domain. Firstly, Arnold transform is improved by adding the number of variables and expanding transformation space. The security of watermark is enhanced by adding more secret keys. When the scrambled watermark is embedded into a carrier image, in order to minimize the damage to watermarked carrier image, normalization processing of watermark is added to the output of hidden layer with watermark under an established BP neural network. The compressed watermarked image is further decompressed to obtain new watermarked carrier image. In the extraction process, through dividing the watermarked image into subblocks and training the BP neural network in compressed domain again, the difference between the original and the new output of hidden layer is calculated. By using improved Arnold inverse transformation for embedding positions, the watermark coordinates are obtained with anti-normalization processing of the difference for extracting watermark. Finally, the presented algorithms have been extensively tested with different conventional signal processing and geometric attacks to verify robustness. Experimental results demonstrate that the proposed scheme has superior performance on imperceptibility and robustness over some existing algorithms with a similar approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Wen XB, Zhang H, Xu XQ, Quan JJ (2009) A new watermarking approach based on probabilistic neural network in wavelet domain. Soft Comput 13:355–360

    Article  Google Scholar 

  2. Gupta M, Parmar G, Gupta R, Saraswat M (2015) Discrete wavelet transform-based color image watermarking using uncorrelated color space and artificial bee colony. Int J Comput Intell Syst 8:364–380

    Article  Google Scholar 

  3. Wang XY, Niu PP, Yang HY et al (2014) A new robust color image watermarking using local quaternion exponent moments. Inf Sci 277:731–754

    Article  Google Scholar 

  4. Anurag M, Charu A, Arpita S, Punam B (2014) Optimized gray-scale image watermarking using DWT-SVD and firefly algorithm. Expert Syst Appl 41:7858–7867

    Article  Google Scholar 

  5. Maryam A, Mansoor R, Hamidreza A (2015) A novel robust scaling image watermarking scheme based on Gaussian Mixture Model. Expert Syst Appl 42:1960–1971

    Article  Google Scholar 

  6. Tsai HH, Liu CC (2011) Wavelet-based image watermark with visibility range estimation based on HVS and neural networks. Pattern Recogn 44:751–763

    Article  Google Scholar 

  7. Mao L, Fan YY, Wang HQ, Lv GY (2011) Fractal and neural networks based watermark identification. Multimed Tools Appl 52:201–219

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Ali M, Ahn CW, Pant M (2014) A robust image watermarking technique using SVD and differential evolution in DCT domain. Opt-Int J Light Electron Opt 125:428–434

    Article  Google Scholar 

  10. Rawat S, Raman B (2011) A chaotic system based fragile watermarking scheme for image tamper detection. AEU-Int J Electron Commun 65:840–847

    Article  Google Scholar 

  11. Chen YL, Yau HT, Yang GJ (2013) A maximum entropy-based chaotic time-variant fragile: watermarking scheme for image tampering detection. Entropy 15:3170–3185

    Article  MathSciNet  Google Scholar 

  12. Sui LS, Gao B (2013) Color image encryption based on gyrator transform and Arnold transform. Opt Laser Technol 48:530–538

    Article  Google Scholar 

  13. Niu PP, Wang P, Liu YN, Yang HY, Wang XY (2016) Invariant color image watermarking approach using quaternion radial harmonic Fourier moments. Multimed Tools Appl 75:7655–7679

    Article  Google Scholar 

  14. He XS, Zhu T, Yang GB (2015) A geometrical attack resistant image watermarking algorithm based on histogram modification. Multidimens Syst Signal Process 26:291–306

    Article  MathSciNet  Google Scholar 

  15. Pun CM, Yuan XC (2010) Robust and geometric invariant watermarking scheme using block and gray-level histograms. Int J Digit Content Technol Appl 4:171–183

    Article  Google Scholar 

  16. Lee Y, Kim J (2012) Histogram rotation-based image watermarking with reversibility. Int J Secur Appl 6:197–201

    Google Scholar 

  17. Rawat S, Raman B (2011) A chaos-based robust watermarking algorithm for rightful ownership protection. Int J Image Graph 11:471–493

    Article  MathSciNet  Google Scholar 

  18. Tsai TH, Wu CY, Fang CL (2014) Design and implementation of a joint data compression and digital watermarking system in an MPEG-2 video encoder. J Signal Process Syst 74:203–220

    Article  Google Scholar 

  19. Busch C, Funk W, Wolthusen S (1999) Digital watermarking from concept to real-time video applications. IEEE Comput Graph Appl 19:25–35

    Article  Google Scholar 

  20. Kumaki T, Nakao K, Hozumi K et al (2014) Development of compression tolerable and highly implementable watermarking method for mobile devices. IEICE Trans Inf Syst E97.D97(3):593–596

    Article  Google Scholar 

  21. Zhang W, Frank Y (2011) Semi-fragile spatial watermarking based on local binary pattern operators. Opt Commun 284:3904–3912

    Article  Google Scholar 

  22. Amit P, Santi PM, Mrinal M (2012) Novel wavelet-based QIM data hiding technique for tamper detection and correction of digital images. J Vis Commun Image Represent 23:454–466

    Article  Google Scholar 

  23. Huo YR, He HJ, Chen F (2014) A semi-fragile image watermarking algorithm with two-stage detection. Multimed Tools Appl 72:123–149

    Article  Google Scholar 

  24. Qi XJ, Xin X (2011) A quantization-based semi-fragile watermarking scheme for image content authentication. J Vis Commun Image Represent 22:187–200

    Article  Google Scholar 

  25. Lin SD, Shie SC, Guo JY (2010) Improving the robustness of DCT-based image watermarking against JPEG compression. Comput Stand Interfaces 32:54–60

    Article  Google Scholar 

  26. Nour EG, Kamel EM, Mahmoud M (2011) A novel multi-objective genetic algorithm optimization for blind RGB color image watermarking. In: Proceedings of 7th international conference on signal image technology and internet-based systems, pp 306–313

  27. Hussain AJ, Al-Jumeily D, Radi N, Lisboa P (2015) Hybrid neural network predictive-wavelet image compression system. Neurocomputing 151:975–984

    Article  Google Scholar 

  28. Liu FL, Liu LL (2010) A new watermarking approach based on BP network in wavelet domain. In: Proceedings of IEEE 3rd international congress on image and signal, pp 1142–1145

  29. Liu LC (2007) The progress and analysis of image compression based on BP artificial neural network. Chin J Microcomput Inf 23:2–3

    Google Scholar 

  30. Zhang J, Wang NC, Xiong F (2002) A novel watermarking for images using neural network. In: Proceedings of IEEE international conference on machine learning and cybernetics, pp 1405–1408

  31. Li GL, Tan YH, Xuan KC et al (2010) Multiwavelet-based image watermarking using SVD and BP neural network. Hebei J Ind Sci Technol 27:219–222

    Google Scholar 

  32. Piao CR, Guan Q, Choi JR, Han SS (2007) Robust digital image watermarking algorithm using RBF neural networks in DWT domain. Int J Fuzzy Log Intell Syst 7:143–147

    Article  Google Scholar 

  33. Ma YD, Qi CL, Qian ZB et al (2006) A novel image compression coding algorithm based on pulse-coupled neural network and gram–schmidt orthogonal base. Chin Acta Electron Sin 34:1256–1259

    Google Scholar 

  34. Zhao J, Zhou MQ, Xie HM et al (2004) A novel wavelet image watermarking scheme combined with chaos sequence and neural network. Lect Notes Comput Sci 3174:663–668

    Article  Google Scholar 

  35. Zhou YM, Zhang C, Zhang ZK (2008) Fast hybrid fractal image compression using an image feature and neural network. Chaos Solitons Fractals 37:623–631

    Article  Google Scholar 

  36. Agilandeeswari L, Ganesan K (2015) A robust color video watermarking scheme based on hybrid embedding techniques. Multimedia Tools and Applications. doi:10.1007/s11042-015-2789-9

    Article  Google Scholar 

  37. Wang J, Qu Q, Liu YD (2011) Modeling of yield strength for IF steel based on BP neural network. In: Proceedings of IEEE 3rd international conference on awareness science and technology, pp 107–110

  38. Yu F, Xu XZ (2014) A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network. Appl Energy 134:102–113

    Article  Google Scholar 

  39. Yi QH, Wang K (2009) An improved watermarking method based on neural network for color image. In: Proceedings of international conference on mechatronics and automation, pp 3113–3117

  40. Chu Y, Mi H, Ji Z, Shao ZB (2008) Image compression using multilayer neural networks based on Fast Bacterial Swarming algorithm. In: Proceedings of international conference on machine learning and cybernetics, pp 2890–2893

  41. Ren F, Ma SC (2014) Multi-class BP neural network classifier based on the conditional log-likelihood. Chin Comput Syst Appl 23:183–186

    Google Scholar 

  42. Xu XB, Zhang KF, Li D, Yang YX, Niu XX (2012) Routing lookup algorithm based on parallel BP neural network. Chin J Commun 33:61–68

    Google Scholar 

  43. Marco B, Davide C, Victor P (2016) A modular framework for color image watermarking. Signal Process 119:102–114

    Article  Google Scholar 

  44. Marco B, Davide C, Victor P (2015) Fragile watermarking using Karhunen–Loeve transform: the KLT-F approach. Soft Comput 19:1905–1919

    Article  Google Scholar 

  45. Mehta R, Rajpal N, Vishwakarma VP (2016) LWT-QR decomposition based robust and efficient image watermarking scheme using Lagrangian SVR. Multimed Tools Appl 75:4129–4150

    Article  Google Scholar 

  46. Nazeer M, Nargis B (2015) Digital image watermarking using partial pivoting lower and upper triangular decomposition into the wavelet domain. IET Image Process 9:795–803

    Article  Google Scholar 

  47. Ali M, Ahn CW (2014) An optimized watermarking technique based on self-adaptive DE in DWT–SVD transform domain. Signal Process 94:545–556

    Article  Google Scholar 

  48. Sun L, Xu JC, Zhang XX, Tian Y (2015) An image watermarking scheme using Arnold transform and fuzzy smooth support vector machine. Math Probl Eng 2015: Article ID 931672

    Google Scholar 

  49. Shao XG, Sun TK, Ding B, Wang XY (2011) Image watermarking algorithm based on artificial neural networks classification. Chin J Comput Appl 31:1505–1507

    Google Scholar 

  50. Khaled L, Jean-Yves C, Mohamed HT (2011) Optimal image watermarking algorithm based on LWT-SVD via multi-objective ant colony optimization. J Inf Hiding Multimed Signal Process 2:303–319

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers and Editor-in-Chief for their valuable comments and hard work on this study. This work was supported by National Natural Science Foundation of China (Nos. 61402153, 61370169, U1304607), Project funded by China Postdoctoral Science Foundation (No. 2016M602247), Key Project of Science and Technology Department of Henan Province (Nos. 142102210056, 162102210261), Key Research Project of High School of Henan Province (No. 16A520015), Ph.D. Research Foundation of Henan Normal University (No. qd15132), and Fund for Youth Key Teachers of Henan Normal University.

Author information

Authors and Affiliations

  1. College of Computer and Information Engineering, Henan Normal University, Xinxiang, 453007, Henan, China

    Lin Sun, Jiucheng Xu, Shangwang Liu, Shiguang Zhang, Yuan Li & Chang’an Shen

  2. Engineering Technology Research Center for Computing Intelligence and Data Mining, Xinxiang, Henan Province, China

    Lin Sun, Jiucheng Xu, Shangwang Liu, Shiguang Zhang & Yuan Li

Authors
  1. Lin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiucheng Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shangwang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shiguang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chang’an Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lin Sun.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, L., Xu, J., Liu, S. et al. A robust image watermarking scheme using Arnold transform and BP neural network. Neural Comput & Applic 30, 2425–2440 (2018). https://doi.org/10.1007/s00521-016-2788-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2788-4

Keywords

Search

Navigation