针对 JPEG 图像通用隐写检测中检测效率低、训练时间长的问题，提出一种基于集成分类器的新检测方法。算法以 CC-PEV 为特征对图像进行描述并作为隐写分析特征；然后，随机构造若干个特征子空间，用 bootstrap 方法构造图像训练子集，分别进行训练得到数个基分类器；根据基分类器的分类结果赋予基分类器不同的权重，将基分类器的结果按照其权重进行融合得到最终的结果。本文对该算法进行了测试，对它的集成性、检测准确率和训练时间进行分析。实验结果表明，相对于传统的集成方法，本文方法用自举方法构造训练集、随机方法构造子特征空间、赋予基分类器不同权重进行融合能够显著地提高算法准确率。本文方法相对于 SVM 和传统的集成分类方法，具有更高的检测率，对于特征维数更大的图像检测，具有更好的拓展性和一般适用性。
Aiming at the problem of the high dimension features and complicated training in image uni-versal steganalysis technology, this paper proposes a steganalysis method based on ensemble classifier.The method designs a ensemble classifier which combines the difference of base classifier and weighted fusion.Specifically,the algorithm uses CC-PEV as steganalysis features.Then,the method randomly structures several feature subspace,uses the bootstrap method to construct image training subsets,and train them to get a number of base classifiers.Finally,every base classifier is given weight that different from each other according to the classification result and then the final forecast is got after the combination of classification results with their respective weight.The experi-mental results show that using ensemble classifier for training and giving base classifiers with different weights fusion could effectively improve the accuracy of the algorithm.
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