A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines
IH '02 Revised Papers from the 5th International Workshop on Information Hiding
Perturbed quantization steganography with wet paper codes
Proceedings of the 2004 workshop on Multimedia and security
Contourlet Spectral Histogram for Texture Classification
SSIAI '06 Proceedings of the 2006 IEEE Southwest Symposium on Image Analysis and Interpretation
IIH-MSP '08 Proceedings of the 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing
YASS: yet another steganographic scheme that resists blind steganalysis
IH'07 Proceedings of the 9th international conference on Information hiding
IH'04 Proceedings of the 6th international conference on Information Hiding
Contourlet based multiresolution texture segmentation using contextual hidden markov models
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
IH'05 Proceedings of the 7th international conference on Information Hiding
Steganalysis using higher-order image statistics
IEEE Transactions on Information Forensics and Security
Directional multiscale modeling of images using the contourlet transform
IEEE Transactions on Image Processing
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An ideal steganographic technique embeds secret information into a carrier cover object with virtually imperceptible modification of the cover object. Steganalysis is a technique to discover the presence of hidden embedded information in a given object. Each steganalysis method is composed of feature extraction and feature classification components. Using features that are more sensitive to information hiding yields higher success in steganalysis. So far, several steganalysis methods have been presented which extract some features from DCT or wavelet coefficients of images. Multi-scale and time-frequency localization of an image is offered by wavelets. However, wavelets are not effective in representing the images in different directions. Contourlet transform addresses this problem by providing two additional properties, directionality and anisotropy. The present paper offers an universal approach to steganalysis called CBS, which uses statistical moments of contourlet coefficients as features for analysis. After feature extraction, a non-linear SVM classifier is applied to classify cover and stego images. The efficiency of the proposed method is demonstrated by experimental investigations. The proposed steganalysis method is compared with two well-known steganalyzers against typical steganography methods. The results showed the superior performance of our method.