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Derivation of error distribution in least-squares steganalysis (2007)
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| Venue: | IEEE Transactions on Information Forensics and Security |
| Citations: | 8 - 4 self |
BibTeX
@ARTICLE{Ker07derivationof,
author = {Andrew D. Ker},
title = {Derivation of error distribution in least-squares steganalysis},
journal = {IEEE Transactions on Information Forensics and Security},
year = {2007},
pages = {140--148}
}
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Abstract
Abstract—This paper considers the least squares method (LSM) for estimation of the length of payload embedded by least-significant bit replacement in digital images. Errors in this estimate have already been investigated empirically, showing a slight negative bias and substantially heavy tails (extreme outliers). In this paper, (approximations for) the estimator distribution over cover images are derived: this requires analysis of the cover image assumption of the LSM algorithm and a new model for cover images which quantifies deviations from this assumption. The theory explains both the heavy tails and the negative bias in terms of cover-specific observable properties, and suggests improved detectors. It also allows the steganalyst to compute precisely, for the first time, a-value for testing the hypothesis that a hidden payload is present. This is the first derivation of steganalysis estimator performance. Index Terms—Least-significant bit (LSB) embedding, steganography, structural steganalysis. I.
Keyphrases
least-squares steganalysis error distribution heavy tail cover image index term least-significant bit estimator distribution extreme outlier structural steganalysis quantifies deviation steganalysis estimator performance lsm algorithm least-significant bit replacement digital image new model cover image assumption hidden payload improved detector slight negative bias cover-specific observable property first time first derivation negative bias
