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This paper reviews the current soft computing (SC) techniques employed in image steganography as well as proposes a new hybrid approach of these SC techniques to exploit their complementary strengths. Four main SC techniques in image steganography - neural network (NN), genetic algorithm (GA), support vector machines (SVM) and fuzzy logic (FL) are assessed based on the three main measurements of steganography...
We develop a new approach for gender recognition. In this paper, our approach uses the rectangle feature vector (RFV) as a representation to identify humans' gender from their faces. The RFV is computationally fast and effective to encode intensity variations of local regions of human face. By only using few rectangle features learned by AdaBoost, we present a gender identifier. We then use nonlinear...
In this research, we introduce a new blind steganalysis in detecting grayscale JPEG images. Features-pooling method is employed to extract the steganalytic features and the classification is done by using neural network. Three different steganographic models are tested and classification results are compared to the five state-of-the-art blind steganalysis.
Hyperspectral remote sensing, as a new remote sensing technology, provides a powerful measure to object identification and classification accurately due to its acuminous ability of spectrum detection. This paper addresses the problem of the classification of hyperspectral remote-sensing images, and studies a novel spectral matching method based on the popular Scale-invariant Feature Transform (SIFT)...
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