Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Efficient image steganography using graph signal processing

© The Institution of Engineering and Technology
Received 07/09/2017, Accepted 19/01/2018, Revised 24/12/2017, Published 23/01/2018

Steganography is used for secret or covert communication. A graph wavelet transform-based steganography using graph signal processing (GSP) is presented, which results in better visual quality stego image as well as extracted secret image. In the proposed scheme, graph wavelet transforms of both the cover image and transformed secret image (using Arnold cat map) are taken followed by alpha blending operation. The GSP-based inverse wavelet transform is performed on the resulting image, to get the stego image. Here, the use of GSP increases the inter-pixel correlation that results in better visual quality stego and extracted secret image as shown in simulation results. Simulation results show that the proposed scheme is more robust than other existing steganography techniques.

Inspec keywords: inverse transforms; graph theory; wavelet transforms; steganography; image processing

Other keywords: alpha blending operation; visual quality stego; graph wavelet transform-based steganography; interpixel correlation; GSP-based inverse wavelet transform; secret communication; graph signal processing; covert communication; image steganography; visual quality stego image

Subjects: Integral transforms; Combinatorial mathematics; Data security; Integral transforms; Optical, image and video signal processing; Cryptography; Combinatorial mathematics; Computer vision and image processing techniques

References

    1. 1)
      • 26. Pradhan, A., Sahu, A.K., Swain, G., et al: ‘Performance evaluation parameters of image steganography techniques’. Int. Conf. Research Advances in Integrated Navigation Systems., 2016, pp. 1–8.
    2. 2)
      • 21. Kabi, K.K., Pradhan, C.: ‘Comparative study of image encryption using 2D chaotic map’. IEEE Int. Conf. Information Systems and Computer Networks, 2014, pp. 105–108.
    3. 3)
      • 7. Rabie, T.: ‘Digital image steganography, an FFT approach’, in Proc. Networked Digital Technologies: 4th Int. Conf., Dubai, UAE, April 2012, pp. 217–230.
    4. 4)
      • 19. Perraudin, N., Paratte, J., Shuman, D., et al: ‘GSPBOX: a toolbox for signal processing on graphs’, March 2016, pp. 1–99.
    5. 5)
      • 11. Rloul, O., Vetterli, M.: ‘Wavelets and signal processing’, IEEE Signal Process. Mag., 1991, 18, (4), pp. 14–38.
    6. 6)
      • 4. Morkel, T., Eloff, J.H.P., Olivier, M.S.: ‘An overview of image steganography’. Proc. Fifth Annual Information Security South Africa Conf., June 2015.
    7. 7)
      • 22. Bhattacharyya, S., Sanyal, G.: ‘A robust image steganography using DWT difference modulation (DWTDM)’, Int. J. Comput. Netw. Inf. Secur., 2012, 7, pp. 27–40.
    8. 8)
      • 23. Prabakaran, G., Bhavani, R.: ‘A modified secure digital image steganography based on discrete wavelet transform’. Int. Conf. Computing, Electronics and Electrical Technologies, March 2012, pp. 1096–1100.
    9. 9)
      • 20. Khalili, M., Asatryan, D.: ‘Colour spaces effects on improved discrete wavelet transform-based digital image watermarking using Arnold transform map’, IET Signal Process., 2013, 7, pp. 177–187.
    10. 10)
      • 1. Feng, B., Lu, W., Sun, W.: ‘Secure binary image steganography based on minimizing the distortion on the texture’, IEEE Trans. Inf. Forensics Secur., 2015, 10, (2), pp. 243–255.
    11. 11)
      • 30. Jebamalar Leavline, E., Sutha, S., Asir Antony Gnana Singh, D.: ‘An analysis of computational complexity of multiscale image representation schemes’. National Conf. Emerging Trends in Electrical and Electronics Engineering, At University College of Engineering Panruti, Tamilnadu, India, March 2013.
    12. 12)
      • 24. Archana, S., Antony Judice, A., Kaliyamurthie, K.P.: ‘A novel approach on image steganographic methods for optimum hiding capacity’, Int. J. Eng. Comput. Sci., 2013, 2, pp. 378–385.
    13. 13)
      • 6. Shejul, A.A., Kulkarni, U.L.: ‘A DWT based approach for steganography using biometrics’. IEEE Int. Conf. Data Storage and Data Engineering, February 2010, pp. 39–43.
    14. 14)
      • 13. Zhang, X.: ‘Design of orthogonal graph wavelet filter banks’. IEEE annual Conf. (IECON), October 2016, pp. 889–894.
    15. 15)
      • 14. Narang, S.K., Ortega, A.: ‘Perfect reconstruction two-channel wavelet filter banks for graph structured data’, IEEE Trans. Signal Process., 2012, 60, (6), pp. 2786–2799.
    16. 16)
      • 10. Sifuzzaman, M., Islam, M.R., Ali, M.Z.: ‘Application of wavelet transform and its advantages compared to Fourier transform’, J. Phys. Sci., 2009, 13, pp. 121–134.
    17. 17)
      • 2. Mahajan, P., Gupta, H.: ‘Improvisation of security in image steganography using DWT, huffman encoding & RC4 based LSB embedding’. IEEE Int. Conf. Computing for Sustainable Global Development (INDIACom), February 2016, pp. 523–529.
    18. 18)
      • 28. Narang, S.K., Ortega, A.: ‘Compact support biorthogonal wavelet filter banks for arbitrary undirected graphs’, IEEE Trans. Signal Process., 2013, 61, (19), pp. 4673–4685.
    19. 19)
      • 12. Shuman, D.I., Narang, S.K., Frossard, P., et al: ‘The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains’, IEEE Signal Process., 2013, 30, (3), pp. 83–98.
    20. 20)
      • 29. Wu, L., Zhang, J., Deng, W., et al: ‘Arnold transformation algorithm and anti-Arnold transformation algorithm’. IEEE 1st Int. Conf. Information Science and Engineering, December 2009, pp. 1164–1167.
    21. 21)
      • 27. Sehgal, P., Sharma, V.K.: ‘Performance improvement in discrete wavelet transform based digital image steganography by the use of integer wavelet transform’, Int. J. Eng. Res. Technol., 2013, 2, (6), pp. 972–977.
    22. 22)
      • 16. Hamidi, H., Amirani, M.C., Arashloo, S.R.: ‘Local selected features of dual-tree complex wavelet transform for single sample face recognition’, IET Image Process., 2015, 9, (8), pp. 716–723.
    23. 23)
      • 3. Sehgal, P., Sharma, V.K.: ‘Eliminating cover image requirement in discrete wavelet transform based digital image steganography’, Int. J. Comput. Appl., 2013, 68, (3), pp. 37–42.
    24. 24)
      • 5. Kumar, V., Kumar, D.: ‘Performance evaluation of DWT based image steganography’. IEEE 2nd Int. Advance Computing Conf., February 2010, pp. 223–228.
    25. 25)
      • 9. Mostafa, R., Ali, A.F., EI Taweal, G.: ‘Hybrid curvelet transform and least significant bit for image steganography’. IEEE Seventh Int. Conf. Intelligent Computing and Information Systems, December 2015, pp. 300–305.
    26. 26)
      • 8. Patel, H., Dave, P.: ‘Steganography technique based on DCT coefficients’, Int. J. Eng. Res. Appl., 2012, 2, (1), pp. 713–717.
    27. 27)
      • 15. Hammond, D.K., Vandergheynst, P., Gribonval, R.: ‘Wavelets on graphs via spectral graph theory’, Appl. Comput. Harmon. Anal., 2011, 30, (2), pp. 129–150.
    28. 28)
      • 25. Ganesan, P., Bhavani, P.: ‘A high secure and robust image steganography using dual wavelet and blending model’, J. Comput. Sci., 2013, 9, (3), pp. 277–284.
    29. 29)
      • 18. Da Cunha, A.L., Zhou, J., Do, M.N.: ‘The nonsubsampled contourlet transform: theory, design, and applications’, IEEE Trans. Image Process., 2006, 15, (10), pp. 3089–3101.
    30. 30)
      • 17. Lim, W.-Q.: ‘The discrete shearlet transform: a new directional transform and compactly supported shearlet frames’, IEEE Trans. Image Process., 2010, 19, (5), pp. 1166–1180.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2017.0965
Loading

Related content

content/journals/10.1049/iet-ipr.2017.0965
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address