Although current blind image steganalysis systems utilize a wide variety of features and classifiers, a common shortcoming in all of them is that they almost have similar processes for all images and they do not take advantage of the content diversity of different images. In this paper, a new framework is proposed that enables us to employ the content of images in these systems. All blind image steganalysis methods can be adapted to the proposed framework. In the training phase of our framework, the input images are first divided into classes according to an image content evaluation criterion and then the training process is specialized for each class. In the testing phase, a fuzzy approach is used to include different classes in the decision making process. Experimental results demonstrate that the proposed framework significantly enhance the detection accuracy of these systems.