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Proceedings Paper

Statistical modeling and steganalysis of DFT-based image steganography
Author(s): Ying Wang; Pierre Moulin
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Paper Abstract

An accurate statistical model of cover images is essential to the success of both steganography and steganalysis. We study the statistics of the full-frame two-dimensional discrete Fourier transform (DFT) coefficients of natural images and show that the independently and identically distributed model with unit exponential distribution is not a sufficiently accurate description of the statistics of normalized image periodograms. Consequently, the stochastic quantization index modulation (QIM) algorithm that aims at preserving this model is detectable in principle. To discriminate the resulted stegoimages from cover images, we train a learning system on them. Building upon a state-of-the-art steganalysis method using the statistical moments of wavelet characteristic functions, we propose new features that are more sensitive to data embedding. The addition of these features significantly improves the steganalyzer's receiver operating characteristic (ROC) curve.

Paper Details

Date Published: 15 February 2006
PDF: 11 pages
Proc. SPIE 6072, Security, Steganography, and Watermarking of Multimedia Contents VIII, 607202 (15 February 2006); doi: 10.1117/12.642357
Show Author Affiliations
Ying Wang, Univ. of Illinois at Urbana-Champaign (United States)
Pierre Moulin, Univ. of Illinois at Urbana-Champaign (United States)

Published in SPIE Proceedings Vol. 6072:
Security, Steganography, and Watermarking of Multimedia Contents VIII
Edward J. Delp III; Ping Wah Wong, Editor(s)

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