通信学报 ›› 2013, Vol. 34 ›› Issue (Z2): 1-3.doi: 10.3969/j.issn.1000-436x.2013.Z2.001

• 网络工程 •    下一篇

基于灰度—梯度共生矩阵的图像型拉圾邮件识别方法

冯兵1,2,李芝棠1,2,3,花广路1,2   

  1. 1 华中科技大学 计算机科学与技术学院,湖北 武汉 430074
    2 下一代互联网接入系统国家工程实验室,湖北 武汉 430074
    3 华中科技大学 网络与计算中心,湖北 武汉 430074
  • 出版日期:2013-12-25 发布日期:2017-06-16

Image spam identification method based on gray-gradient co-occurrence matrix

Bing FENG1,2,Zhi-tang LI1,2,3,Gang-lu HUA1,2   

  1. 1 School of Computer Science and Technology,Huazhong University of Science and Technology,Wuhan 430074,China
    2 National Engineering Laboratory for Next Generation Internet Access System,Wuhan 430074,China
    3 Network and Computing Center,Huazhong University of Science and Technology,Wuhan 430074,China
  • Online:2013-12-25 Published:2017-06-16

摘要:

为了逃避基于文本的垃圾邮件系统的检测,越来越多的垃圾邮件制造者将文本信息嵌入到图像中。为了有效地检测出图像型垃圾邮件,提出了一种基于灰度—梯度共生矩阵(GGCM,gray-gradient co-occurrence matrix)的图像型垃圾邮件识别方法。先通过灰度—梯度共生矩阵提取图像的特征信息,然后运用最小二乘支持向量机(LS-SVM,least squares support vector machines)进行分类。实验表明,该方法具有较高的分类精度和较好的实时性。

关键词: 图像型垃圾邮件, 灰度—梯度共生矩阵, 最小二乘支持向量机, 纹理特征

Abstract:

In order to avoid the detection of the spam system based on text,more and more spammers have embedded text information into the image.An image spam identification method based on gray-gradient co-occurrence matrix (GGCM) was proposed to detect image spam effectively.The feature of image was extracted through GGCM firstly,and then LS-SVM was used to do classification.The test results show that this method has higher classification accuracy and better real-time performance

Key words: image spam, gray-gradient co-occurrence matrix, LS-SVM, texture feature