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In this paper, we propose a feature generation and classification approach for universal steganalysis based on genetic algorithm (GA) and higher order statistics. The GA is utilized to select a subset of candidate features, a subset of candidate transformations to generate new features. The logistic regression model and Bayesian network model are then used as the classifier. Experimental results show...
In this paper, we propose a feature selection and transformation approach for universal steganalysis based on genetic algorithm (GA) and higher order statistics. We choose three types of typical statistics as candidate features and twelve kinds of basic functions as candidate transformations. The GA is utilized to select a subset of candidate features, a subset of candidate transformations and coefficients...
In this paper, we propose a feature selection and transformation approach for universal steganalysis based on genetic algorithm (GA) and higher order statistics. We choose three types of typical statistics as candidate features and twelve kinds of basic functions as candidate transformations. The GA is utilized to select a subset of candidate features, a subset of candidate transformations and coefficients...
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