There is an urgent demand on steganalysis, which analyzes if an image includes hidden information, or further decodes
the hidden information. This paper proposes a steganalysis method based on statistics model in contourlet transform
domain. The proposed method is a blind universal steganalysis method, which does not aim at specified steganography
method. The contourlet coefficients of natural image shows obvious regularity which includes sparsity and clustering in
subband and similarity across scales. The popular statistical model in wavelet subband is the generalized Gaussian
distribution (GGD) model, which can capture the first-order statistical features in subband. While the GGD model can
not characterize the dependency between coefficients. The proposed steganalysis method takes contourlet statistics in
subband and dependency between contourlet coefficients into account. The dependencies are measured using mutual
information. The selected features include parameters of GGD model in subband, the mutual information between
coefficients. The classificator chose is Support Vector Machine (SVM). The experimental results show that the features
used in the proposed method are valid, when the dependencies between contourlet coefficients are taken into account, the
false positive rate is greatly lower than the case in which the dependencies are not considered.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.