Skip to main content
SpringerLink
Log in

A secure visual secret sharing (VSS) scheme with CNN-based image enhancement for underwater images

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Nowadays, underwater images are being used to identify various important resources like objects, minerals, and valuable metals. Due to the wide availability of the Internet, we can transmit underwater images over a network. As underwater images contain important information, there is a need to transmit them securely over a network. Visual secret sharing (VSS) scheme is a cryptographic technique, which is used to transmit visual information over insecure networks. Recently proposed randomized VSS (RVSS) scheme recovers secret image (SI) with a self-similarity index (SSIM) of 60–80%. But, RVSS is suitable for general images, whereas underwater images are more complex than general images. In this paper, we propose a VSS scheme using super-resolution for sharing underwater images. Additionally, we have removed blocking artifacts from the reconstructed SI using convolution neural network (CNN)-based architecture. The proposed CNN-based architecture uses a residue image as a cue to improve the visual quality of the SI. The experimental results show that the proposed VSS scheme can reconstruct SI with almost 86–99% SSIM.

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

Similar content being viewed by others

References

  1. Ateniese, G., Blundo, C., De Santis, A., Stinson, D.R.: Visual cryptography for general access structures. Inf. Comput. 129(2), 86–106 (1996)

    Article  MathSciNet  Google Scholar 

  2. Ateniese, G., Blundo, C., De Santis, A., Stinson, D.R.: Extended capabilities for visual cryptography. Theoret. Comput. Sci. 250(1), 143–161 (2001)

    Article  MathSciNet  Google Scholar 

  3. Bertsekas, D.P.: Nonlinear Programming. Athena Scientific, Belmont (1999)

    MATH  Google Scholar 

  4. Chang, C.C., Chen, T.S., Chung, L.Z.: A steganographic method based upon jpeg and quantization table modification. Inf. Sci. 141(1), 123–138 (2002)

    Article  Google Scholar 

  5. Chen, S.K.: Friendly progressive visual secret sharing using generalized random grids. Opt. Eng. 48(11), 117001–1 (2009)

    Article  Google Scholar 

  6. Dian, R., Li, S., Guo, A., Fang, L.: Deep hyperspectral image sharpening. IEEE Trans. Neural Netw. Learn. Syst. 99, 1–11 (2018)

    MathSciNet  Google Scholar 

  7. Dong, C., Deng, Y., Change Loy, C., Tang, X.: Compression artifacts reduction by a deep convolutional network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 576–584 (2015)

  8. Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016)

    Article  Google Scholar 

  9. Duarte, A., Codevilla, F., Gaya, J.D.O., Botelho, S.S.: A dataset to evaluate underwater image restoration methods. In: OCEANS 2016-Shanghai, pp. 1–6. IEEE (2016)

  10. Eskicioglu, A.M., Fisher, P.S.: Image quality measures and their performance. IEEE Trans. Commun. 43(12), 2959–2965 (1995)

    Article  Google Scholar 

  11. Fan, D.P., Lin, Z., Zhang, Z., Zhu, M., Cheng, M.M.: Rethinking RGB-D salient object detection: models, data sets, and large-scale benchmarks. IEEE Trans Neural Netw Learn Syst (2020)

  12. Fang, W.P.: Friendly progressive visual secret sharing. Pattern Recognit. 41(4), 1410–1414 (2008)

    Article  Google Scholar 

  13. Fu, K., Fan, D.P., Ji, G.P., Zhao, Q.: Jl-dcf: Joint learning and densely-cooperative fusion framework for rgb-d salient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3052–3062 (2020)

  14. Fu, K., Zhao, Q., Gu, I.Y.H., Yang, J.: Deepside: a general deep framework for salient object detection. Neurocomputing 356, 69–82 (2019)

    Article  Google Scholar 

  15. Glasby, G.: Lessons learned from deep-sea mining. Science 289(5479), 551–553 (2000)

    Article  Google Scholar 

  16. Gujjunoori, S., Amberker, B.: BUSYEMBED: an HVS based reversible data embedding scheme for video using DCT (2013)

  17. Gujjunoori, S., Amberker, B.: DCT based reversible data embedding for MPEG-4 video using HVS characteristics. J. Inf. Secur. Appl. 18(4), 157–166 (2013)

    Google Scholar 

  18. Halfar, J., Fujita, R.M.: Danger of deep-sea mining. Science 316(5827), 987–987 (2007)

    Article  Google Scholar 

  19. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  20. Hou, Y.C., Quan, Z.Y.: Progressive visual cryptography with unexpanded shares. IEEE Trans. Circuits Syst. Video Technol. 21(11), 1760–1764 (2011)

    Article  Google Scholar 

  21. Hou, Y.C., Quan, Z.Y., Tsai, C.F., Tseng, A.Y.: Block-based progressive visual secret sharing. Inf. Sci. 233, 290–304 (2013)

    Article  Google Scholar 

  22. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of 8th International Conference Computer Vision, vol. 2, pp. 416–423 (2001)

  23. Mhala, N.C., Jamal, R., Pais, A.R.: Randomised visual secret sharing scheme for grey-scale and colour images. IET Image Processing 12, 422–431(9) (2018). http://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0759

  24. Mhala, N.C., Pais, A.R.: Contrast enhancement of progressive visual secret sharing (PVSS) scheme for gray-scale and color images using super-resolution. Sig. Process. 162, 253–267 (2019)

    Article  Google Scholar 

  25. Mhala, N.C., Pais, A.R.: An improved and secure visual secret sharing (VSS) scheme for medical images. In: 2019 11th International Conference on Communication Systems and Networks (COMSNETS), pp. 823–828. IEEE (2019)

  26. Naor, M., Shamir, A.: Visual cryptography. In: Workshop on the Theory and Application of of Cryptographic Techniques, pp. 1–12. Springer, Berlin (1994)

  27. Nocedal, J., Wright, S.J.: Numerical Optimization, 2nd (2006)

  28. Shivani, S.: VMVC: verifiable multi-tone visual cryptography. Multimedia Tools Appl., pp. 1–20 (2017)

  29. Timofte, R., De Smet, V., Van Gool, L.: Anchored neighborhood regression for fast example-based super-resolution. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1920–1927 (2013)

  30. Timofte, R., De Smet, V., Van Gool, L.: A+: Adjusted anchored neighborhood regression for fast super-resolution. In: Asian Conference on Computer Vision, pp. 111–126. Springer, Berlin (2014)

  31. Wang, R.Z.: Region incrementing visual cryptography. IEEE Signal Process. Lett. 16(8), 659–662 (2009)

    Article  Google Scholar 

  32. Wang, R.Z., Lee, Y.K., Huang, S.Y., Chia, T.L.: Multilevel visual secret sharing. In: Innovative Computing, Information and Control, 2007. ICICIC’07. Second International Conference on, pp. 283–283. IEEE (2007)

  33. Wang, Z., Arce, G.R., Di Crescenzo, G.: Halftone visual cryptography via error diffusion. IEEE Trans. Inf. Forensics Secur. 4(3), 383–396 (2009)

    Article  Google Scholar 

  34. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  35. Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: International Conference on Curves and Surfaces, pp. 711–730. Springer, Berlin (2010)

  36. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: residual learning of deep cnn for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)

    Article  MathSciNet  Google Scholar 

  37. Zhao, J.X., Liu, J.J., Fan, D.P., Cao, Y., Yang, J., Cheng, M.M.: EGNet: Edge guidance network for salient object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 8779–8788 (2019)

  38. Zhou, Z., Arce, G.R., Di Crescenzo, G.: Halftone visual cryptography. IEEE Trans. Image Process. 15(8), 2441–2453 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Information Security Research Laboratory, Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, India

    Nikhil C. Mhala & Alwyn R. Pais

Authors
  1. Nikhil C. Mhala
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alwyn R. Pais
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nikhil C. Mhala.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mhala, N.C., Pais, A.R. A secure visual secret sharing (VSS) scheme with CNN-based image enhancement for underwater images. Vis Comput 37, 2097–2111 (2021). https://doi.org/10.1007/s00371-020-01972-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-020-01972-9

Keywords

Search

Navigation