A Discriminative Classifier Learning Approach to Image Modeling and Spam Image Identification

We propose a discriminative classifier learning approach to image modeling for spam image identification. We analyze a large number of images extracted from the SpamArchive spam corpora and identify four key spam image properties: color moment, color heterogeneity, conspicuousness, and self-similarity. These properties emerge from a large variety of spam images and are more robust than simply using visual content to model images. We apply multi-class characterization to model images sent with emails. A maximal figure-of-merit (MFoM) learning algorithm is then proposed to design classifiers for spam image identification. Experimental results on about 240 spam and legitimate images show that multi-class characterization is more suitable than singleclass characterization for spam image identification. Our proposed

• Type: Conference
• Year: 2007
• Where: CEAS
• Authors: Byungki Byun, Chin-Hui Lee, Steve Webb, Calton Pu
• Keywords: CEAS 2007, Internet Technology, Multi-class Characterization, Spam Image, Spam Image Identification