Mixing high-dimensional features for JPEG steganalysis with ensemble classifier
This paper proposes a JPEG steganalysis scheme based on the ensemble classifier and high-dimensional feature space. We first combine three current feature sets and remove the unimportant features according to the correlation between different features parts so as to form a new feature space used for steganalysis. This way, the dependencies among cover and steganographic images can be still represented by the features with a reduced dimensionality. Furthermore, we design a proportion mechanism to manage the feature selection in two subspaces for each base learner of the ensemble classifier. Experimental results show that the proposed scheme can effectively defeat the MB and nsF5 steganographic methods and its performance is better than that of existing steganalysis approaches.
steganalysis ensemble classifier high-dimensional feature space co-occurrence matrices proportion criterion random subspace