Image spam classification based on low-level image features

Author

Wang, Chao ; Zhang, Fengli ; Li, Fagen ; Liu, Qiao

Author_Institution

Sch. of Comput. Sci. & Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

fYear

2010

fDate

28-30 July 2010

Firstpage

290

Lastpage

293

Abstract

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 demonstrate that these features are effective for detecting image spam and comparable to other cutting-age alternatives.

Keywords

electronic mail; feature extraction; image classification; information filtering; radial basis function networks; security of data; support vector machines; RBF kernel; feature extraction; image spam classification; image spam detection; kernel function; low-level image features; metadata; one-class SVM classifier; spam filter; visual feature; Accuracy; Feature extraction; Image coding; Support vector machines; Unsolicited electronic mail;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications, Circuits and Systems (ICCCAS), 2010 International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-8224-5

Type

conf

DOI

10.1109/ICCCAS.2010.5581998

Filename

5581998