Paper
4 March 2015 Content-adaptive pentary steganography using the multivariate generalized Gaussian cover model
Vahid Sedighi, Jessica Fridrich, Remi Cogranne
Author Affiliations +
Proceedings Volume 9409, Media Watermarking, Security, and Forensics 2015; 94090H (2015) https://doi.org/10.1117/12.2080272
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Vahid Sedighi, Jessica Fridrich, and Remi Cogranne "Content-adaptive pentary steganography using the multivariate generalized Gaussian cover model", Proc. SPIE 9409, Media Watermarking, Security, and Forensics 2015, 94090H (4 March 2015); https://doi.org/10.1117/12.2080272
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Cited by 59 scholarly publications.
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KEYWORDS
Distortion

Sensors

Error analysis

Statistical modeling

Information security

Steganalysis

Steganography

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