An image steganography method based on integer wavelet transform

An image steganography method based on integer wavelet transform This paper presents a novel approach for Image steganography based on Integer Wavelet Transform. In this method, the cover image is mapped to a specific frequency domain. Then in the obtained frequency domain, edge coefficients are classified based on their MSBs. The suggested method prevents changes in MSB in a way that receiver can extract the information without any mistakes. Considering the preformed classification, secret bits will be embedded in the frequency coefficients and then with the use of inverse transformation, stego image will be obtained. The most important features that our work obtained are good adaptation with human vision system and retrieval of data without error. Simulation results show that our proposed method has a good adaptation with human vision system (HVS) and outperforms in terms of PSPNR factors over recently published works. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

An image steganography method based on integer wavelet transform

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4935-z
Publisher site
See Article on Publisher Site

Abstract

This paper presents a novel approach for Image steganography based on Integer Wavelet Transform. In this method, the cover image is mapped to a specific frequency domain. Then in the obtained frequency domain, edge coefficients are classified based on their MSBs. The suggested method prevents changes in MSB in a way that receiver can extract the information without any mistakes. Considering the preformed classification, secret bits will be embedded in the frequency coefficients and then with the use of inverse transformation, stego image will be obtained. The most important features that our work obtained are good adaptation with human vision system and retrieval of data without error. Simulation results show that our proposed method has a good adaptation with human vision system (HVS) and outperforms in terms of PSPNR factors over recently published works.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jun 28, 2017

References

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