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Implementation of Least Significant Bit Steganography and statistical steganalysis

Published:26 October 2012Publication History

ABSTRACT

Least Significant Bit (LSB) Steganography is a method for hidden information in such a way that can only be detected by its intended recipient. Steganography in Digital media becomes a challenging discipline, since the Human Auditory System (HAS) is highly sensitive. One of the main obstacles of the number of files hiding in Digital media is to develop a system which has the quality to include number of files and without affectively the Digital media and provides Statistical steganalysis schemes detect the existence of secret information embedded by steganography. The detection and regular singular (RS) attack methods are two well known statistical stegnalysis schemes used against LSB. The embedded message length can be estimated accurately by these two steganalysis schemes. For secret communication, the resistance of steganography against steganalysis is very important for information security. To avoid enemy's attempts, the statistical features between stego-media and cover-media should be as similar as possible for better resistance to steganalysis. In this paper we propose a reversible histogram transformation function based LSB steganographic method to resist statistical steganalysis.

The cover data should not be significantly degraded by the embedded data and the embedded data should be as imperceptible as possible. The embedded data should be as immune as possible to modifications from intelligent attacks or anticipated manipulations. Thus it is necessary that the hidden message should be encrypted. Steganograph is an application of Digital media(Image, Audio, Video) steganography. It hides data in digital media files.

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  1. Implementation of Least Significant Bit Steganography and statistical steganalysis

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            CCSEIT '12: Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
            October 2012
            800 pages
            ISBN:9781450313100
            DOI:10.1145/2393216

            Copyright © 2012 ACM

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 26 October 2012

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