On the influence of feature selection and extraction for the classification of steganalysis based on the JPEG image

Steganalysis analyzes the existence of embedded secret information in cover. It can be divided into two categories, special steganalysis and blind steganalysis, according to the feature extraction and classification. Steganalysis can be assigned to classification problems, which based on the training set to determine the suspicious cover's class label. Essentially, it is two-class problem in pattern recognition or machine learning. Selecting the right feature for classification is very important and difficult. There has been little research that deals with the feature selection and feature extraction problem with specific respect to steganalysis. This paper studies the influence of selected feature to the steganalysis. It is crucial that selected features are very sensitive to the embedding changes, but insensitive to the image content. First, the basic framework is described for image steganalysis, which includes five parts: training/testing images set, feature search/selection, feature extraction/feature vectors, training classifier, training model and classifier parameter estimator. We then classify the existing feature according to the domain which belonging to. Finally, we do the experiment to compare the performance of different feature by use different classifier, such as ANN and SVM. Through our experiment, although we use small data set, but we find out the optimized features for classification.