[1]黄曜[],许华虎[],欧阳杰臣[],等. 针对图像来源鉴别中支持向量机的研究[J].计算机技术与发展,2016,26(10):1-5.
　HUANG Yao[],XU Hua-hu[],OUYANG Jie-chen[],et al. Research on Support Vector Machines for Image Source Identification[J].,2016,26(10):1-5.
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 针对图像来源鉴别中支持向量机的研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年10期
页码:
1-5
栏目:
智能、算法、系统工程
出版日期:
2016-10-10

文章信息/Info

Title:
 Research on Support Vector Machines for Image Source Identification
文章编号:
1673-629X（2016)10-0001-05
作者:
 黄曜[1]; 许华虎[2]; 欧阳杰臣[1]; 高珏[3]
 1.上海大学 计算机工程与科学学院；2.上海上大海润信息系统有限公司；3.上海大学 计算中心
Author(s):
 HUANG Yao[1] ; XU Hua-hu[2]; OUYANG Jie-chen[1]; GAO Jue[3]
关键词:
 ; 图像盲取证; 支持向量机分类模型; 核函数; 核参数; 图像来源鉴别率
Keywords:
 ; blind image forensics; SVM classification model; kernel function; kernel parameter; image source identification rate
分类号:
TP31
文献标志码:
A
摘要:
 随着数码图像的普及，图像盲取证成为时下的研究热点之一，如何识别图像来源是其主要的研究内容。作为图像来源鉴别最关键的阶段，构造鉴别的支持向量机( SVM)分类模型直接影响最终的鉴别率。由于不同核函数以及核参数对分类器性能有着相异的影响，故分析对比了各种核函数，然后选取了细分效果更好的高斯径向基函数作为核函数。针对核参数选择问题，分析了各种核参数寻优算法，并通过实验验证了各个算法的效果，以及最终构造的分类模型的效果。实验结果表明，选用高斯径向基函数作为核函数，利用粒子群算法选出的核参数所构造的分类模型取得了最好的图像来源鉴别率。
Abstract:
 With the popularity of digital images,blind image forensics has become one of the hotspots nowadays. The main research con-tent of blind image forensics is how to identify the image source. As the most critical stage of image source identification,the SVM classi-fication model for identification directly affects the final identification rate. Because the different kernel function and kernel parameters has distinct effect on the performance of the classification model,the various kernel functions are analyzed and compared,then the Gaussian radial basis function with better subdivision is selected as the kernel function. In view of the kernel parameter selection,the various kernel parameter optimization algorithms are analyzed,and the effectiveness of each algorithm and the effect of the final classification model by experiments are verified. The results show that choosing Gaussian radial basis function as the kernel function,using the kernel parameters selected by particle swarm algorithm to construct the classification model will achieve the best image source identification rate.

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更新日期/Last Update: 2016-11-25