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Contentadaptive pentary steganography using the multivariate generalized gaussian cover model
 IS&T/SPIE Electronic Imaging conf
"... 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 text ..."
Abstract

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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 syndrometrellis codes, are computed by solving a pair of nonlinear algebraic equations. Using rich models and selectionchannelaware features, we compare the security of our scheme based on the generalized Gaussian model with pentary versions of two popular embedding algorithms: HILL and SUNIWARD. 1.
On dangers of crossvalidation in steganalysis On dangers of crossvalidation in steganalysis
"... Modern steganalysis is a combination of a feature space design and a supervised binary classification. In this report, we assume that the feature space has been already constructed, i.e., the steganalyst has a set of training features and needs to train a binary classifier. Any machine learning tool ..."
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Modern steganalysis is a combination of a feature space design and a supervised binary classification. In this report, we assume that the feature space has been already constructed, i.e., the steganalyst has a set of training features and needs to train a binary classifier. Any machine learning tool can be used for this task and its parameters can be tuned through crossvalidation, a standard automated modelselection procedure. However, classification problems arising in steganalysis have a very specific nature – individual training samples naturally form pairs of cover–stego feature vectors with opposite labels lying close to each other in the feature space. It is important to preserve these cover–stego pairs during crossvalidation (prevent splitting each pair
Modified BCH data hiding scheme for JPEG steganography
, 2012
"... In this article, a new BoseChaudhuriHochquenghem (BCH)based data hiding scheme for JPEG steganography is presented. Traditional data hiding approaches hide data into each block, where all the blocks are not overlapping each other. However, in the proposed method, two consecutive blocks can be o ..."
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In this article, a new BoseChaudhuriHochquenghem (BCH)based data hiding scheme for JPEG steganography is presented. Traditional data hiding approaches hide data into each block, where all the blocks are not overlapping each other. However, in the proposed method, two consecutive blocks can be overlapped to form a combined block which is larger than a single block, but smaller than two consecutive nonoverlapping blocks in size. In order to embed more amounts of data into the combined block than a single block, the BCHbased data hiding scheme has to be redesigned. In this article, we propose a way to get a joint solution for hiding data into two blocks with intersected coefficients such that any modification of the intersected area does not affect the data hiding process into both blocks. Due to hiding more amounts of data into the intersected area, embedding capacity is increased. On the other hand, the nonzero DCT coefficient stream is modified to achieve better steganalysis and to reduce the distortion impact after data hiding. This approach carefully inserts or removes 1 or1 coefficients into or from the DCT coefficient stream according to the rule proposed in this article. Experimental results show that the proposed algorithms work well and their performance is significant.