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An efficient feature extraction methodology for blind image steganalysis using contourlet transform and zernike moments
, Article 2013 10th International ISC Conference on Information Security and Cryptology, ISCISC 2013 ; Aug , 2013 , Page(s): 1 - 6 ; Ghaemmaghami, S ; Sharif University of Technology
IEEE Computer Society
2013
Abstract
We propose an effective blind image steganalysis based on contourlet transform and Zernike moments that improves the detection accuracy of universal image steganalysis methods. The proposed method examines randomness in the test image to distinguish between the stego and non-stego images. The suspicious image is decomposed by contourlet transform, and then the absolute Zernike moments of contourlet subbands coefficients of the image and linear prediction error of each contourlet subband are extracted as features for steganalysis. These features are fed to a nonlinear SVM classifier with an RBF kernel to distinguish between cover and stego images. Experimental results show that the proposed...
Improve Performance of Higher Order Statistics in Spatial and Frequency Domains in Blind Image Steganalysis
, M.Sc. Thesis Sharif University of Technology ; Ghaemmaghami, Shahrokh (Supervisor)
Abstract
Blind image steganalysis is a technique used to, which require no prior information about the steganographic method applied to the stego im- age, determine whether the image contains an embedded message or not. The basic idea of blind steganalysis is to extract some features sensitive to information hiding, and then exploit classifiers for judging whether a given test image contains a secret message.The main focus of this research is to design an choose features sen-sitive to the embedding changes. In fact, we use high order moments in different domains, such as spatial, DCT and multi-resolution do-main, in order to improve the performance of existing steganalyzers.Accordingly, First, we...