An Investigation of Genetic Algorithm on Steganalysis Techniques

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 of the logistic regression model for blind image steganalysis. The logistic regression model is then used as the classifier. Experimental results show that the GA based approach increases the blind detection accuracy and also provides a good generality by identifying an untrained stego-algorithm.