Abstract
Since the risk associated with transmission of data over networks is the chance of intrusion and hampering of secrecy, safe transmission of hidden image without hindering the cover image is expected as in image steganography. In this paper, a spatial domain type of image authentication technique by means of Genetic Algorithm has been proposed. Genetic Algorithm is used to improve the quality of stego image. High PSNR values are achieved for various images in comparison with some other existing techniques in this field.
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References
Roy, R., et al.: Optimization of stego image retaining information using genetic algorithm with 8-connected PSNR. Procedia Comput. Sci. 60, 468–477 (2015)
Cheddad, A., et al.: A secure and improved self-embedding algorithm to combat digital document forgery. Signal Process. 89(12), 2324–2332 (2009)
Wang, S., et al.: A secure steganography method based on genetic algorithm. J. Inf. Hiding Multimed. Signal Process. 1(1), 28–35 (2010). ISSN 2073-421
Begum, R., et al.: Best approach for LSB based steganography using genetic algorithm and visual cryptography for secured data hiding and transmission over networks. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(6) (2014)
Li, X., et al.: A steganographic method based upon JPEG and particle swarm optimization algorithm. Inf. Sci. 177(15), 3099–3109 (2007)
Fard, M.A., et al.: A new genetic algorithm approach for secure JPEG steganography. In: IEEE International Conference Engineering of Intelligent Systems (2006). doi:10.1109/ICEIS.2006.1703168
Chang, C.C., et al.: Sharing secrets in stego image with authentication. Sci. Direct Pattern Recogn. 41(10), 3130–3137 (2008)
Wang, R.Z., et al.: Hiding data in images by optimal moderately significant bit-replacement. IEEE Electron. Lett. 36(25), 2069–2070 (2000)
Wang, R.Z., et al.: Image hiding by optimal LSB substitution and genetic algorithm. Pattern Recogn. 34, 671–683 (2001)
Jung, K.H.: High-capacity steganographic method based on pixel value differencing and LSB replacement methods. Imaging Sci. J. 54(4), 213–221 (2010)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Boston (1989). ISBN 0-201-15767-5
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1998). (NB original printing 1975). ISBN 0-262-58111-6
Vo-Van, T., et al.: Modified genetic algorithm-based clustering for probability density functions. J. Stat. Comput. Simul. 87, 1964–1979 (2017)
Jiang, N., Zhao, N., Wang, L.: Int. J. Theor. Phys. 55: 107 (2016). doi:10.1007/s10773-015-2640-0
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Khamrui, A., Gupta, D.D., Ghosh, S., Nandy, S. (2017). A Spatial Domain Image Authentication Technique Using Genetic Algorithm. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 776. Springer, Singapore. https://doi.org/10.1007/978-981-10-6430-2_45
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DOI: https://doi.org/10.1007/978-981-10-6430-2_45
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