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Proceedings Paper

Content-adaptive pentary steganography using the multivariate generalized Gaussian cover model
Author(s): Vahid Sedighi; Jessica Fridrich; Remi Cogranne
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Paper Abstract

The vast majority of steganographic schemes for digital images stored in the raster format limit the amplitude of embedding changes to the smallest possible value. In this paper, we investigate the possibility to further improve the empirical security by allowing the embedding changes in highly textured areas to have a larger amplitude and thus embedding there a larger payload. Our approach is entirely model driven in the sense that the probabilities with which the cover pixels should be changed by a certain amount are derived from the cover model to minimize the power of an optimal statistical test. The embedding consists of two steps. First, the sender estimates the cover model parameters, the pixel variances, when modeling the pixels as a sequence of independent but not identically distributed generalized Gaussian random variables. Then, the embedding change probabilities for changing each pixel by 1 or 2, which can be transformed to costs for practical embedding using syndrome-trellis codes, are computed by solving a pair of non-linear algebraic equations. Using rich models and selection-channel-aware features, we compare the security of our scheme based on the generalized Gaussian model with pentary versions of two popular embedding algorithms: HILL and S-UNIWARD.

Paper Details

Date Published: 4 March 2015
PDF: 13 pages
Proc. SPIE 9409, Media Watermarking, Security, and Forensics 2015, 94090H (4 March 2015); doi: 10.1117/12.2080272
Show Author Affiliations
Vahid Sedighi, Binghamton Univ. (United States)
Jessica Fridrich, Binghamton Univ. (United States)
Remi Cogranne, Univ. de Technologie Troyes (France)


Published in SPIE Proceedings Vol. 9409:
Media Watermarking, Security, and Forensics 2015
Adnan M. Alattar; Nasir D. Memon; Chad D. Heitzenrater, Editor(s)

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