Skip to main content

Advertisement

SpringerLink
Log in

Robust image steganography approach based on RIWT-Laplacian pyramid and histogram shifting using deep learning

  • Special Issue Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Nowadays, highly sensitive medical images are vulnerable to data threats and privacy attacks. They must be kept secure while transmitting them across insecure channels precisely for this purpose. The robust image steganography is focused on this work by exploiting Redundant Integer Wavelet Transform (RIWT), Laplacian Pyramid, Arnold scrambling and Histogram shifting algorithm to facilitate secure communication of secret images in the context. Stego images thus generated are subjected to a deep learning approach to assess if it can be classified as a cover or not. If not, the HS parameter is modified to generate stego images in such a way to classify it as a cover image. Thus it is difficult to suspect the existence of a secret image by the Human Visual System (HVS). The efficiency of our method is analyzed by comparing it with related methods present in the literature. Average NCC values between the original secret image and the extracted secret image are 0.8917 which is higher than the schemes in the literature. Average PSNR values of the stego image are 36.375 even when the embedding rate is increased to 4 bits per pixel. The analysis was done on security and robustness also reveals better results. From the experimental analysis, it is proved that the proposed method is superior to the related methods of the literature.

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

Similar content being viewed by others

References

  1. Kishor, S. N., GN Kodanda Ramaiah, and S. A. K. Jilani (2016). A review on steganography through multimedia. 2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS). IEEE.

  2. Jero, S. Edward, Palaniappan Ramu, and S. Ramakrishnan(2014). Discrete wavelet transform and singular value decomposition based ECG steganography for secured patient information transmission. Journal of medical systems 38.10 132.

  3. Wang, W., Si, M., Pang, Y., Ran, P., Wang, H., Jiang, X., Liu, Y., Wu, J., Wu, W., Chilamkurti, N., Jeon, G.: An encryption algorithm based on combined chaos in body area networks. Comput. Electr. Eng. 65, 282–291 (2018)

    Article  Google Scholar 

  4. Muhammad, K., et al.: Image steganography using uncorrelated color space and its application for security of visual contents in online social networks. Future Gener. Comput. Syst. 86, 951–960 (2018)

    Article  Google Scholar 

  5. Cheddad, A., et al.: Digital image steganography: survey and analysis of current methods. Signal Process. 90(3), 727–752 (2010)

    Article  Google Scholar 

  6. Sreekutty, M. S., Baiju, P.S.: Security enhancement in image steganography for medical integrity verification system. In: 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT). IEEE (2017)

  7. Altaay, A. A. Jabbar, S. B. S., Mazdak Z.: An introduction to image steganography techniques. In: 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT). IEEE (2012)

  8. Shaik, A., Thanikaiselvan, V., Amitharajan, R.: Data security through data hiding in images: a review. J. Artif. Intell. 10(1), 1–21 (2017)

    Article  Google Scholar 

  9. Arunkumar, S., Subramaniyaswamy, S., et al.: RIWT and QR factorization based hybrid robust image steganography using block selection algorithm for IoT devices. J. Intell. Fuzzy Syst. 36(5), 4265–4276 (2019)

    Article  Google Scholar 

  10. Arunkumar, S., Subramaniyaswamy, S., et al.: SVD-based robust image steganographic scheme using RIWT and DCT for secure transmission of medical images. Measurement 139, 426–437 (2019)

    Article  Google Scholar 

  11. Zhang, Yi, et al.: On the fault-tolerant performance for a class of robust image steganography. Signal Process. 146, 99–111 (2018)

    Article  Google Scholar 

  12. Arunkumar, S., Subramaniyaswamy, S., et al.: Meta-data based secret image sharing application for different sized biomedical images. Biomed. Res. 29, S394–S398 (2018)

  13. Bandyopadhyay, D., et al.: A novel secure image steganography method based on chaos theory in spatial domain. Int. J. Secur. Priv. Trust Manag. IJSPTM 3(1), 11–22 (2014)

    Google Scholar 

  14. Rabie, T., Baziyad, M.: Visual fidelity without sacrificing capacity: an adaptive Laplacian pyramid approach to information hiding. J. Electron. Imaging 26(6), 063001 (2017)

    Article  Google Scholar 

  15. Subhedar, M.S., Mankar, V.H.: Current status and key issues in image steganography: a survey. Comput. Sci. Rev. 13, 95–113 (2014)

    Article  Google Scholar 

  16. Peng, F., Li, X., Yang, B.: Adaptive reversible data hiding scheme based on integer transform. Signal Process. 92(1), 54–62 (2012)

    Article  Google Scholar 

  17. Qin, C., Chang, C.C., Huang, Y.H., Liao, L.T.: An inpainting-assisted reversible steganographic scheme using a histogram shifting mechanism. IEEE Trans. Circuits Syst. Video Technol. 23(7), 1109–1118 (2012)

    Article  Google Scholar 

  18. Rabie, T., Baziyad, M., Kamel, I.: Enhanced high capacity image steganography using discrete wavelet transform and the Laplacian pyramid. Multimed. Tools Appl. 77(18), 23673–23698 (2018)

    Article  Google Scholar 

  19. Ni, Z., Shi, Y. Q., Ansari, N., Su, W.: Reversible data hiding. In: IEEE Transactions on Circuits and Systems for Video Technology, pp. 354–362 (2006)

  20. Hussain, S., Keung, J., Akhunzada, A., Ahmad, A., Khan, A.A., Cuomo, S., Piccialli, F., Jeon, G.: Implications of deep learning for the automation of design patterns organization. J. Parallel Distrib. Comput. 117, 256–266 (2018)

    Article  Google Scholar 

  21. Jan, B., Farman, H., Khan, M., Ul Islam, I., Ali, S., Ahmad, A., Jeon, G.: Deep learning in big data analytics: a comparative study. Comput. Electr. Eng. 75, 275–287 (2019)

    Article  Google Scholar 

  22. Baby, D., Thomas, J., Augustine, G., George, E., Michael, N.R.: A novel DWT based image securing method using steganography. Proc. Comput. Sci. 46, 612–618 (2015)

    Article  Google Scholar 

  23. Kini, N.G., Kini, V.G.: A secured steganography algorithm for hiding an image in an image. Integrated Intelligent Computing, Communication and Security, pp. 539–546. Springer, Singapore (2019)

    Chapter  Google Scholar 

  24. Wang, J., Wu, J., Wu, Z., Anisetti, M., Jeon, G.: Bayesian method application for color demosaicking. SPIE Opt. Eng. 57(5), 053102 (2018)

    Google Scholar 

  25. Wu, W., Yang, X., Pang, Y., Peng, J., Jeon, G.: A multifocus image fusion method by using hidden Markov model. Opt. Commun. 287, 63–72 (2013)

    Article  Google Scholar 

  26. Reddy, H.M., Raja, K.B.: High capacity and security steganography using discrete wavelet transform. Int. J. Comput. Sci. Secur. IJCSS 3(6), 462 (2009)

    Google Scholar 

  27. Nag, A., Biswas, S., Sarkar, D., Sarkar, P.P.: A novel technique for image steganography based on DWT and Huffman encoding. Int. J. Comput. Sci. Secur. IJCSS 4(6), 497–610 (2011)

    Google Scholar 

  28. Latef, A.A.: Color image steganography based on discrete wavelet and discrete cosine transforms. Ibn Al-Haitham J. Pure Appl. Sci. 24(3), 296–302 (2011)

    Google Scholar 

  29. Dey, N., Roy, A.B., & Dey, S.: A novel approach of color image hiding using RGB color planes and DWT. arXiv preprint arXiv:1208.0803 (2012)

  30. Jeon, G., Park, S.J., Fang, Y., Anisetti, M., Bellandi, V., Damiani, E., Jeong, J.: Specification of efficient block matching scheme for motion estimation in video compression. SPIE Opt. Eng. 48(12), 127005 (2009)

    Article  Google Scholar 

  31. Shejul, A.A., Kulkarni, U.L.: A secure skin tone based steganography using wavelet transform. Int. J. Comput. Theory Eng. 3(1), 16 (2011)

    Article  Google Scholar 

  32. Kaur, G., Kochhar, A.: Transform domain analysis of image steganography. Int. J. Sci. Emerg. Technol. Latest Trends 6(1), 29–37 (2013)

    Google Scholar 

  33. Singh, A.K., Dave, M., Mohan, A.: Hybrid technique for robust and imperceptible dual watermarking using error correcting codes for application in telemedicine. IJESDF 6(4), 285–305 (2014)

    Article  Google Scholar 

  34. Nagpal, S., Bhushan, S., Mahajan, M.: An enhanced digital image watermarking scheme for medical images using neural network, DWT and RSA. Int. J. Mod. Educ. Comput. Sci. 8(4), 46 (2016)

    Article  Google Scholar 

  35. Thakkar, F.N., Srivastava, V.K.: A blind medical image watermarking: DWT-SVD based robust and secure approach for telemedicine applications. Multimed. Tools Appl. 76(3), 3669–3697 (2017)

    Article  Google Scholar 

  36. Kalita, M., Tuithung, T., Majumder, S.: A new steganography method using integer wavelet transform and least significant bit substitution. Comput. J. 62, 1639–1655 (2019)

  37. Rathore, M.M., Paul, A., Ahmad, A., Anisetti, M., Jeon, G.: Hadoop-based intelligent care system (HICS): analytical approach for big data in IoT. ACM Trans. Internet Technol. 18(1), 1–24 (2017)

    Article  Google Scholar 

  38. Burt, P., Adelson, E.: The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 31(4), 532–540 (1983)

    Article  Google Scholar 

  39. Swain, G., Lenka, S.K.: A hybrid approach to steganography embedding at darkest and brightest pixels. In: 2010 International Conference on Communication and Computational Intelligence (INCOCCI), pp. 529–534. IEEE (2010)

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

    Article  Google Scholar 

  41. Zhang, Z., Wu, L., Gao, S., Sun, H., Yan, Y.: Robust reversible watermarking algorithm based on RIWT and compressed sensing. Arab. J. Sci. Eng. 43(2), 979–992 (2018)

    Article  Google Scholar 

  42. Ye, J., Ni, J., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inf. Forensics Secur. TIFS 12(11), 2545–2557 (2017)

    Article  Google Scholar 

  43. Zhang, Y., Zhang, W., Chen, K., Liu, J., Liu, Y., Yu, N.: Adversarial examples against deep neural network based steganalysis. In: Proceedings of the 6th ACM workshop on information hiding and multimedia security, IH&MMSec'2018, pp. 67–72, Innsbruck, Austria (2018)

  44. Goljan, M., Fridrich, J., Cogranne, R.: Rich model for steganalysis of color images. In: 2014 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 185–190. IEEE (2014)

  45. Ye, J., Ni, J., Yi, Y.: Deep learning hierarchical representations for image steganalysis. IEEE Trans. Inf. Forensics Secur. 12(11), 2545–2557 (2017)

    Article  Google Scholar 

  46. Chen, J., Kang, X., Liu, Y., Wang, Z.J.: Median filtering forensics based on convolutional neural networks. IEEE Signal Process. Lett. 22(11), 1849–1853 (2015)

    Article  Google Scholar 

  47. Lin, C.C., Shiu, P.F.: High capacity data hiding scheme for DCT-based images. J. Inf. Hiding Multimed. Signal Process. 1(3), 220–240 (2010)

    Google Scholar 

  48. Li, F., Mao, Q., Chang, C.C.: Reversible data hiding scheme based on the Haar discrete wavelet transform and interleaving prediction method. Multimed. Tools Appl. 77(5), 5149–5168 (2018)

    Article  Google Scholar 

  49. Shaik, A., Thanikaiselvan, V.: A new image-based hybrid reversible data hiding model using IHWT and RP-PEHM for secured data communication. Circuits Syst. Signal Process. 37(11), 4907–4928 (2018)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing, SASTRA Deemed University, Thanjavur, India

    Arunkumar Sukumar & V. Subramaniyaswamy

  2. Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India

    Logesh Ravi

  3. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    V. Vijayakumar

  4. School of Electrical Engineering, Vellore Institute of Technology, Vellore, India

    V. Indragandhi

Authors
  1. Arunkumar Sukumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Subramaniyaswamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Logesh Ravi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. V. Vijayakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. V. Indragandhi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. Subramaniyaswamy.

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

Sukumar, A., Subramaniyaswamy, V., Ravi, L. et al. Robust image steganography approach based on RIWT-Laplacian pyramid and histogram shifting using deep learning. Multimedia Systems 27, 651–666 (2021). https://doi.org/10.1007/s00530-020-00665-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-020-00665-6

Keywords

Search

Navigation