An Adaptive Ensemble Classifier for Steganalysis Based on Dynamic Weighted Fusion

摘要

Recently, ensemble classifier is predominantly used for steganalysis of digital media, due to its efficiency when working with high-dimensional feature sets and large databases. While fusing the decisions of many weak base classifiers, the majority voting rule is often used, which has the disadvantage that all the classifiers have the same authority regardless of their individual classification abilities. In this paper, we propose a new dynamic weighted fusion method for steganalysis which can be adaptive to input testing samples. For each testing sample, the weight of each base classifier is dynamically assigned according to the distance between the testing sample and the classifier. Experimental results show that the proposed method is able to increase steganalysis performance.