Towards Genetic Feature Selection in Image Steganalysis

In this study, a new feature-based steganalytic method is presented and four classification methods: Fisher linear discriminant, Gaussian naive Bayes, multilayer perceptron, and k nearest neighbor, are compared for steganalysis of suspicious images. The method exploits statistics of the histogram, wavelet statistics, amplitudes of local extrema from the ID and 2D adjacency histograms, center of mass of the histogram characteristic function and co-occurrence matrices for feature extraction process. In order to reduce the proposed features dimension and select the best subset, genetic algorithm is used and the results are compared through principle component analysis and linear discriminant analysis. The results show that the proposed method achieves higher accuracy in discriminating between innocent and stego images, as compared to one of wellknown image steganalysis schemes.