Image Spam Classification based on low-level Image Features

摘要：

As image spam becomes widespread and does a lot of harm, it is more important to filter such spam effectively for now. In this paper, We propose a feature extraction scheme that focus on low-level features (metadata and visual features) of image, which can making classification rapid. They are effective because of not rely on extracting text and analyzing the content of email. a one-class SVM classifier with RBF kernel as the kernel function is used to detect image spam. Experimental results demontrate that these features are effective for detecting image spam and comparable to other cutting-age alternatives.

作者: Chao Wang Fengli Zhang Fagen Li Qiao Liu

作者单位: School of Computer Science &Engineering University of Electronic Science & Technology of China, Chengdu, Sichuan, China

会议类型: 国际会议

会议名称: 2010 International Conference on Communications,Circuits and Systems(2010年通信、电路与系统国际会议)

会议地点: 成都

会议语种:英文

页码: 290-293

在线出版日期: 2010-06-28（万方平台首次上网日期，不代表论文的发表时间）