Authors
Vahid Sedighi, Jessica Fridrich, Rémi Cogranne,
Title
Content-Adaptive Pentary Steganography Using the Multivariate Generalized Gaussian Cover Model
In
Proc. SPIE, Electronic Imaging, Media Watermarking, Security, and Forensics
Volume
9409
Pages
12
Publisher
SPIE
Year
2015
Publisher's URL
http://spie.org/EI/conferencedetails/media-watermarking-security-forensics
Indexed by
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.
Affiliations
Offprint