Feature selection for image spam classification

摘要：

This paper considers the low-level feature modeling problem in image spam classification, in which most of the prevalent content based spam filters are shown to be inefficient because their OCR procedure are vulnerable to text obscuring attacks from spammers. We first built up a basic feature set through a low-level feature extraction process, and then proposed a stepwise regression method to determine the best subset automatically, which was controlled by a minimum description length criterion. Experimental results indicate that the proposed approach is very effective for the purpose of modeling spam images, and the selected feature set is applicable for practical anti-spam tasks, its performance is comparable to some other cutting-edge approaches.

作者: Qiao Liu Feng-li Zhang Zhi-guang Qin Chao Wang Shuang Chen Qiu-ming Ma

作者单位: School of Computer Science and Engineering, University of Electronic Science and Technology of China Faculty of School of Computer Science and Engineering, University of Electronic Science and Technolo

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: 294-297

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）