Embedding change rate estimation based on ensemble learning

In order to achieve higher estimation accuracy of the embedding change rate of a stego object, an ensemble learning-based estimation method is presented. First of all, a framework of embedding change rate estimation based on estimator ensemble is proposed. Then an algorithm of building the estimator ensemble, the core of the framework, is concretely described. Finally, a pruning method for estimator ensemble is proposed in consideration of both the diversity among the base estimators and accuracy of each of them. The experimental results for three modern steganographic algorithms (nsF5, PQ and PQt) indicate that the proposed method acquired better performance than the existed typical method. Furthermore, the pruned estimator ensemble with less base estimators maintained, even slightly improved the estimation accuracy, compared to the one without purning.