Reduction of Markov Extended Features in JPEG Image Steganalysis
Jing-qu Lin, Xiaodong Wang, Shangping Zhong · 2009
The Markov extended features extraction performs well in JPEG image steganalysis. The dimensionality of the feature space is 324. However, the high-dimensional feature space does some side-effects to classifiers. In this paper, we combine the forward selection algorithm with F-score method to select the Markov extended features. We then compress those selected features to get a smaller feature set according to their directions. Therefore, the dimensionality of feature space is reduced from 324 to 26. The experimental results are presented to demonstrate that our proposed scheme decreases complexity of classifiers' training but maintaining the correct classification rate.