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Proceedings Paper

Minimax eigenvector decomposition for data hiding
Author(s): Jennifer Davidson
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Paper Abstract

Steganography is the study of hiding information within a covert channel in order to transmit a secret message. Any public media such as image data, audio data, or even file packets, can be used as a covert channel. This paper presents an embedding algorithm that hides a message in an image using a technique based on a nonlinear matrix transform called the minimax eigenvector decomposition (MED). The MED is a minimax algebra version of the well-known singular value decomposition (SVD). Minimax algebra is a matrix algebra based on the algebraic operations of maximum and addition, developed initially for use in operations research and extended later to represent a class of nonlinear image processing operations. The discrete mathematical morphology operations of dilation and erosion, for example, are contained within minimax algebra. The MED is much quicker to compute than the SVD and avoids the numerical computational issues of the SVD because the operations involved only integer addition, subtraction, and compare. We present the algorithm to embed data using the MED, show examples applied to image data, and discuss limitations and advantages as compared with another similar algorithm.

Paper Details

Date Published: 16 September 2005
PDF: 10 pages
Proc. SPIE 5915, Mathematics of Data/Image Coding, Compression, and Encryption VIII, with Applications, 59150T (16 September 2005); doi: 10.1117/12.615271
Show Author Affiliations
Jennifer Davidson, Iowa State Univ. (United States)

Published in SPIE Proceedings Vol. 5915:
Mathematics of Data/Image Coding, Compression, and Encryption VIII, with Applications
Mark S. Schmalz, Editor(s)

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