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Adaptive LSB Steganography Based on Chaos Theory and Random DistortionTUTUNCU, K. , DEMIRCI, B.
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ciphers, chaos, data encapsulation, data security, digital images
image(17), steganography(15), chaotic(11), hiding(10), steganographic(9), information(8), algorithm(8), communications(7), signal(5), security(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2018-08-31
Volume 18, Issue 3, Year 2018, On page(s): 15 - 22
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.03003
Web of Science Accession Number: 000442420900003
SCOPUS ID: 85052105181
Image steganography is a technique to hide secret information in an image without leaving any apparent evidence of image alteration. Hiding capacity, perceptual transparency, robustness, and resistance against attack must be considered as characteristics of the image steganography algorithms. In this study, Improved Chaos Based Bit Embedding has been proposed as a new steganography algorithm. It is based on two basic principles. One of them is determining the bits in which the secret data will be embedded by logistic map and the other one is embedding the secret data into only one of the three color channels that is chosen randomly. It distorts the other remaining channels so that it is harder to obtain the text within the image by an unwanted person. The proposed algorithm has been tested on 10 sample images along with the four basic steganography algorithms: Least Significant Bit Embedding, Pseudo Random Least Significant Bit Embedding, EzStego, and F5. It has been seen that generating unpredictable indexes by the chaotic random number generators, and embedding the secret data into only one of the three channels (distorting remaining channels) increased resistance against attacks. Perceptual transparencies and capacity ratio of the proposed algorithm are compatible with the other four algorithms.
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 A Modified Hash Based Least Significant Bits Algorithm for Steganography, Muyco, Stella D., Hernandez, Alexander A., Proceedings of the 2019 4th International Conference on Big Data and Computing - ICBDC 2019, ISBN 9781450362788, 2019.
Digital Object Identifier: 10.1145/3335484.3335514 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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