基于多分类器融合的未知嵌入率图像隐写分析方法

提出一种基于多分类器融合的未知嵌入率图像隐写分析方法.通过建立多个不同嵌入率下的训练分类器模型,得到对测试图像的多个局部决策值;然后将得到的局部决策值转化为证据,并根据各分类器的漏检率和虚警率,对各局部决策值分配权重;最后由基于权重系数的D-S(Dempster-Shafer)证据理论推理得到最终的决策.针对LSB匹配隐写的实验结果表明,本文方法改善了未知嵌入率下的隐写检测性能.

Multi Classifier Fusion Based Rate Unknown Image Steganalysis

A rate unknown image steganalysis scheme is proposed based on multiple classifier fusion. Firstly, various classified results are acquired by using the multi-rate classifiers established in the training phase. Secondly, these classified results are converted to evidence and enhanced through introducing weighted coefficients which are acquired according to the missed detection rates and the false alarm rates of different classifiers. Finally, the decision is obtained by Dempster-Shafer(D-S) evidence theory based on weighted coefficients. The detection work is presented to attack LSB matching. Experimental results show that the proposed method improves detection accuracy.