Affiliations: College of Information and Technology, Hebei University of Economics and Business, Shijiazhuang, Hebei, China
Correspondence:
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Corresponding author: Xiuhui Ge, College of Information and Technology, Hebei University of Economics and Business, Shijiazhuang 050061, Hebei, China. E-mail:[email protected]
Abstract:
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.
