【摘要】 针对盲隐写分析中的特征选择问题,提出了结合粒子群优化算法(PSO)的支持向量机分类器进行特征选择的方法。该方法使用非线性支持向量机作为分类器,使用PSO为支持向量机寻找最优的图像特征集合作为训练集和测试集,同时选择最优的支持向量机参数,进而利用最优的特征集和支持向量机参数对隐写图像进行检测。实验结果表明,该优化方法明显优于Farid,ANOVA和F-score方法,提高了检测隐写图像的成功率和系统检测效率。更多还原

【Abstract】 To study the feature selection in blind steganalysis, a new feature selection method based on Particle Swarm Optimization and Support Vector Machine(PSOSVM) is proposed. Using nonlinear SVM as classifier, this method employs the Particle Swarm Optimization(PSO) algorithm to find the best image feature sets as training and testing sets and chooses the best Support Vector Machine(SVM) parameters at the same time. Then the selected image feature sets and parameters are used to detect the stego-images. In order to demonstrate its validity,the proposed method is compared with several existing methods by experiment. The experimental results show that the proposed method outperforms the Farid, Analysis of Variation(ANOVA) and F-score methods. It has higher recognition ratio of stego-images and improves the detection efficiency.更多还原