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Proceedings Paper

Taxonomy for spatial domain LSB steganography techniques
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Paper Abstract

The Least Significant Bit (LSB) embedding technique is a well-known and broadly employed method in multimedia steganography, used mainly in applications involving single bit-plane manipulations in the spatial domain [1]. The key advantages of LSB procedures are they are simple to understand, easy to implement, have high embedding capacity, and can be resistant to steganalysis attacks. Additionally, the LSB approach has spawned numerous applications and can be used as the basis of more complex techniques for multimedia data embedding. In the last several decades, hundreds of new LSB or LSB variant methods have been developed in an effort to optimize capacity while minimizing detectability, taking advantage of the overall simplicity of this method. LSB-steganalysis research has also intensified in an effort to find new or improved ways to evaluate the performance of this widely used steganographic system. This paper reviews and categorizes some of these major techniques of LSB embedding, focusing specifically on the spatial domain. Some justification for establishing and identifying promising uses of a proposed SD-LSB centric taxonomy are discussed. Specifically, we define a new taxonomy for SD-LSB embedding techniques with the goal of aiding researchers in tool classification methodologies that can lead to advances in the state-of-the-art in steganography. With a common framework to work with, researchers can begin to more concretely identify core tools and common techniques to establish common standards of practice for steganography in general. Finally, we provide a summary on some of the most common LSB embedding techniques followed by a proposed taxonomy standard for steganalysis.

Paper Details

Date Published: 22 May 2014
PDF: 15 pages
Proc. SPIE 9120, Mobile Multimedia/Image Processing, Security, and Applications 2014, 912006 (22 May 2014); doi: 10.1117/12.2054368
Show Author Affiliations
James C. Collins, The Univ. of Texas at San Antonio (United States)
Sos S. Agaian, The Univ. of Texas at San Antonio (United States)


Published in SPIE Proceedings Vol. 9120:
Mobile Multimedia/Image Processing, Security, and Applications 2014
Sos S. Agaian; Sabah A. Jassim; Eliza Yingzi Du, Editor(s)

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