随着特征选择和分类技术研究的不断深入，盲检测的精度越来越高，但现有方法大多不考虑图像自身的内容特性对检测的影响. 该文提出一种基于图像内容和特征融合的盲检测方法，根据图像复杂度将待检测图像划分为不同的子图像库，以巴氏距离度量各局部特征的分类能力并确定权值，在特征融合基础上对各子库提取不同特征，用支持向量机进行分类. 在混合图像库上进行的实验表明，该方法具有更好的检测性能，并降低了运算复杂度.

With increasing research on image feature vector extraction and classification, blind steganalysis is becoming more efficient and accurate. However, many existing methods use similar processing for all images without taking account the diverse image contents. This paper proposes a new approach based on
image contents and feature fusion. The input images are divided into several classes according to the content complexity before feature extraction. Bhattacharyya distance is used to evaluate the usefulness of individual features and determine their weights. Steganalysis is subsequently conducted using a fusing approach and a support vector machine (SVM) classifier in a decision making process. Experimental results on several sets of  images demonstrate that the proposed steganalyzer outperforms some previous methods. It provides reliable results with reduced computational complexity.