从净图角度出发，提出了以BMP、JPEG净图特征为基础，采用FCM聚类的多超球体一类分类的隐藏信息检测技术。该技术针对同一类样本的特征存在着部分差异的特点，先将净图样本进行模糊C均值聚类，再将该样本的各子类样本特征输入一类SVM分类器进行训练，建立净图样本各子类的超球体分类模型，以此解决净图检测正确率低的问题。实验结果表明，该方法具有一定的通用性和泛化能力，减少了虚警率和漏检率。

The main focus in this paper is the detection techniques based on the cover images, which are detected using FCM OC SVM. The feature set of cover samples are firstly clustered by the FCM algorithm. Then, the sub class data are trained separately and the multi hyper spheres classification models are established. This technology can improve the detection of cover image and stego image and decrease false detection. Meanwhile the effect of many coefficients on the detecting accuracy is analyzed and generalized for broad application.