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Proceedings Paper

T-order statistics and secure adaptive steganography
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Paper Abstract

Adaptive steganography is a statistical approach for hiding the digital information into another form of digital media. The goal is to ensure the changes introduced into the cover image remain consistent with the natural noise model associated with digital images. There are generally two classes of steganography − global and local. The global class encompasses all non-adaptive techniques and is the simplest to apply and easiest to detect. The second classification is the local class, which defines most of the present adaptive techniques. We propose a new adaptive technique that is able to overcome embedding capacity limitations and reduce the revealing artifacts that are customarily introduced when applying other embedding methods. To obtain the objectives, we introduce a third faction which is the pixel focused class of steganography. Applying a new adaptive T-order statistical local characterization, the proposed algorithm is able to adaptively select the number of bits to embed per pixel. Additionally, a histogram retention process, an evaluation measure based on the cover image and statistical analysis allow for the embedding of information in a manner which ensures soundness from multiple statistical aspects. Based on the results of simulated experiments, our method is shown to securely allow an increased amount of embedding capacity, simultaneously avoiding detection by varying steganalysis techniques.

Paper Details

Date Published: 30 August 2005
PDF: 10 pages
Proc. SPIE 5916, Mathematical Methods in Pattern and Image Analysis, 591609 (30 August 2005); doi: 10.1117/12.618122
Show Author Affiliations
Sos S. Agaian, Univ. of Texas at San Antonio (United States)
Ronnie R. Sifuentes, Univ. of Texas at San Antonio (United States)
Ravindranath Cherukuri, Univ. of Texas at San Antonio (United States)


Published in SPIE Proceedings Vol. 5916:
Mathematical Methods in Pattern and Image Analysis
Jaakko T. Astola; Ioan Tabus; Junior Barrera, Editor(s)

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