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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7/2012

01.10.2012 | Original Article

Improved contourlet-based steganalysis using binary particle swarm optimization and radial basis neural networks

verfasst von: Mansour Sheikhan, Mansoureh Pezhmanpour, M. Shahram Moin

Erschienen in: Neural Computing and Applications | Ausgabe 7/2012

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Abstract

Steganography is the science of hiding information in a media such as video, image or audio files. On the other hand, the aim of steganalysis is to detect the presence of embedded data in a given media. In this paper, a steganalysis method is presented for the colored joint photographic experts group images in which the statistical moments of contourlet transform coefficients are used as the features. In this way, binary particle swarm optimization algorithm is also employed as a closed-loop feature selection method to select the efficient features in tandem with improvement of the detection rate. Nonlinear support vector machine and two variants of radial basis neural networks, i.e., radial basis function and probabilistic neural network, are used as the classification tools and their performance is compared in detecting the stego and clean images. Experimental results show that even for low embedding rates, the detection accuracy of the proposed method is more than 80% along with 30% reduction in the size of feature set.
Vorheriger Artikel Decouple implementation of weight decay for recursive least square
Nächster Artikel Sequential Row–Column 2DPCA for face recognition

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Metadaten
Titel
Improved contourlet-based steganalysis using binary particle swarm optimization and radial basis neural networks
verfasst von
Mansour Sheikhan
Mansoureh Pezhmanpour
M. Shahram Moin
Publikationsdatum
01.10.2012
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 7/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-011-0729-9

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