典型源相机分类算法性能研究  

Study on performance of typical source camera classification algorithms

作　　者：周长辉[1] 胡永健[2] 谭莉玲[1] 

机构地区：[1]华南理工大学自动化科学与工程学院,广州510641 [2]华南理工大学电子与信息学院,广州510641

出　　处：《计算机应用》2011年第4期1133-1137,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60772115;60572140)

摘　　要：现有文献中的源相机分类算法很少讨论测试图像在受到轻微图像处理后算法性能的变化。利用支持向量机,对源相机分类算法的性能和鲁棒性进行了分析,比较了测试图像遭受处理前后分类算法的检测准确率,并研究了图像特征的鲁棒性。由于基于模式分类的算法通常需要精简特征个数以提高执行效率,因此,还讨论了精简模式下相机分类算法的性能以及特征选择对辨识算法鲁棒性的影响。In literature,there are very few discussions on the change of performance of source camera classification algorithms when test images are subjected to minor image processing. Using Support Vector Machines（SVM）,this paper analyzed the performance and robustness of source camera classification algorithms.It compared the detection accuracy for unprocessed images with that for processed images,and investigated the robustness of different types of image features.Since pattern classification-based algorithms often need to reduce the number of image features for computational efficiency,this paper also discussed the performance of camera classification algorithms using the image feature subsets.The impact of using these subsets on the robustness of camera classification algorithms was explored as well.

关 键 词：数字图像取证 源相机分类 支持向量机 特征选择 鲁棒性 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]