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Improving NORMALS Using Modified Baudot-Murray Code

Published: 26 November 2016 Publication History

Abstract

NORMALS or Normal Linguistic Methodology Steganography is a steganography method based on noiseless steganography paradigm or Nostega. In this method, a message is embedded into cover text by modifying the external input of a Natural Language Generation (NLG) system that produces text. The main problem of NORMALS method is small embedding capacity. To solve this problem, this research proposed some method to improve NORMALS method. A better embedding capacity can be achieved by modifying the character encoding used in this research. In addition to modifying the character encoding to make it more efficient, this research also ensures that all the code are evenly distributed, so that in writing the secret message all the code in modified character encoding has almost the same probability to be used. This can reduce suspicion because there is no code that excessively used. The results of the experiments showed that the proposed method has better efficiency in writing the secret message compared to NORMALS, especially for secret messages in the same language with a corpus that is used to modify the character encoding.

References

[1]
A. Desoky, "Nostega: A Novel Noiseless Steganography Paradigm," Journal of Digital Forensic Practice, vol. 2, no. 3, pp. 132--139, 2008.
[2]
A. Desoky, "Graphstega: Graph Steganography Methodology," Journal of Digital Forensic Practice, vol. 2, no. 1, pp. 27--36, January 2008.
[3]
A. Desoky, "Chestega: Chess Steganography Methodology," Journal of Security and Communication Networks, vol. 2, no. 6, pp. 555--566, March 2009.
[4]
A. Desoky, "Edustega: An Education-Centric Steganography Methodology," International Journal of Security and Networks, vol. 6, no. 2/3, pp. 153--173, 2011.
[5]
A. Desoky, "NORMALS: Normal Linguistic Steganography Methodology," Journal of Information Hiding and Multimedia Signal Processing, vol. 1, no. 3, pp. 145--171, July 2010.
[6]
D. Jurafsky dan J. H. Martin, Speech and Language Processing, 2nd Edition, Prentice Hall, 2008.
[7]
E. Reiter dan R. Dale, Building Natural Language Generation Systems (Studies in Natural Language Processing), Cambridge University Press, 2006.

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ICCNS '16: Proceedings of the 6th International Conference on Communication and Network Security
November 2016
133 pages
ISBN:9781450347839
DOI:10.1145/3017971
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 November 2016

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Author Tags

  1. Character Encoding
  2. Steganography
  3. Text Generation

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