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In this paper we present a novel LSB matching steganalysis method based on feature vectors derived from co-occurrence matrix in spatial domain, which is sensitive to data embedding process. This matrix is derived from an image that some of its most significant bit planes are removed. By this preprocessing in addition to decrease the size of feature vector also preserve effects of embedding. We investigate...
In this paper we present a novel steganalysis method with feature vectors derived from gray level co-occurrence matrix (GLCM) in spatial domain, which is sensitive to data embedding process. This GLCM matrix is derived from an image. We consider several combinations of diagonal elements of GLCM as features and use SVM for classification. The experimental results have demonstrated that the proposed...
This paper proposed a new steganalysis scheme of LSB-matching steganography based on statistical moments of the DFT of histogram of multi-level wavelet subbands. Before deriving these wavelet subbands a pre-processing apply to images under the test. The pre-processing contains removing some most significant bit planes. Then we decompose the image using three-level Haar discrete wavelet transform (DWT)...
This paper proposed a new image steganalysis scheme based on statistical moments of histogram of multi-level wavelet subbands in frequency domain. Different frequencies of histogram have different sensitivity to various data embedding. Then we decompose the test image using three-level Haar discrete wavelet transform (DWT) into 13 subbands (here the image itself is considered as the LLO subband)....
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