Efficient image steganography using graph Signal processing

Abstract

 

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

 

Exiting System

In recent years, many multi-scale transforms such as dual-tree complex wavelet [16], shift-invariant shearlet  transform (SIST) [17] and non-sub sampled contourlet transform [18] have been proposed. Many signals have been extended to GSP-based function. The main advantages of using the graph wavelet-based technique are (i) the graph wavelet has greater numerical stability in reconstruction because they provide more localise, temporal and frequency information [12, 15]. (ii) The graph is more suitable for multidimensional data analysis (i.e. image, network etc.) [14]. (iii) The graph wavelet resists altering the target to corrupt or devastate the information itself [19]. The graph wavelet has the features as other multi-scale wavelets have.

 

Proposed System

In this paper, a novel graph wavelet-based steganography scheme is proposed for hiding secret image inside cover image with higher security. The major contribution of graph wavelet in proposed work is to provide high visual quality stego imagebecause it uses inter-pixel relationship or in other word the main goal here is to achieve higher degree of invisible secret image inside the cover image. This is done by performing Arnold cat map transformation before blending operation [20]. The next objective is to achieve perfect visibility of extracted image at the receiver end, which is achieved by using GSP. GSP preserves the neighbourhood information, the reason for its use in the proposed scheme. In simulation result, better visual quality stego image and extracted secret image can be achieved by varying the value of blending coefficient. The proposed scheme involves two main processes: encoding process and decoding process.

 

Conclusion

 

The GSP-based novel highly secure image steganography scheme for grey-scale image has been proposed for concealing a secret  image inside a cover image. The security of secret image is more satisfactory than in the previous works such as [23, 27]. Experimental results show that the proposed method introduced good visible quality in stego image that led to the best secret image imperceptibility property inside the stego image. It is also shown in the results that the NCC value between secret image and extracted secret image is high, which denotes a better visible quality of extracted secret image.

 

References

 

[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

[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

[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

[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

[5] Kumar, V., Kumar, D.: ‘Performance evaluation of DWT based image steganography’. IEEE 2nd Int. Advance Computing Conf., February 2010, pp.

223–228

[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

[7] Rabie, T.: ‘Digital image steganography, an FFT approach’, in Proc. Networked Digital Technologies: 4th Int. Conf., Dubai, UAE, April 2012, pp.

217–230

[8] Patel, H., Dave, P.: ‘Steganography technique based on DCT coefficients’, Int. J. Eng. Res. Appl., 2012, 2, (1), pp. 713–717

[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

[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[11] Rloul, O., Vetterli, M.: ‘Wavelets and signal processing’, IEEE Signal Process. Mag., 1991, 18, (4), pp. 14–38

[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

[13] Zhang, X.: ‘Design of orthogonal graph wavelet filter banks’. IEEE annual Conf. (IECON), October 2016, pp. 889–894

[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

[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