Improved RSM algorithm based on ensemble pruning for high-dimensional steganalysis

Nowadays, there exist a number of challenges in high-dimensional features space for steganalysis, which mainly focus on the difficulty to train classifiers and high computational complexity. Therefore, we propose the improved RSM algorithm based on ensemble pruning for high dimensional steganalysis. Firstly, SFS algorithm is adopted to select the features with high classification ability as fixed features, while the remaining features are selected randomly in the other feature space. Secondly, the feature subset is established using fixed features and the randomly selected features. It is then used to train the base classifiers afterwards. Finally, the pruned ensemble is obtained to yield the final decision using FP-Tree algorithm, in which a FP-Tree is built to compact the prediction results of all base classifiers for the validation set and the ensemble with the best predictive accuracy for the validation set is output. Experiment results demonstrate that the proposed algorithm gains the small-size pruned ensemble and achieves better detection performance with a relatively lower computation overhead when compared with the traditional ensemble pruning algorithms such as Forward Selection and Oriented Order against HUGO steganography.