A Fast Image-Spam Filtering System using Support Vector Machine

Abstract

The explosion of Image spam emails hasprompted the development of numerous spamfiltering techniques. This paper proposes anefficient image spam filtering system using threemethods. The first method, File properties,analyses high level features in order to reducecomputation cost. The second approach usesHue, Saturation, Intensity (HSI) color model ofhistogram and the third method uses Hough lineDetection. These three methods filter the imagespam by analyzing both images including textand image. The images are collected from threedifferent datasets that are Priceton, Image SpamHunter and Spam Archieve Datasets. SupportVector Machine (SVM) classifies the input imageis spam image or normal image. Theexperimental result shows the accuracy ofdifferent methods on different datasets andevaluates computation time. Among the threemethods, Hough line can detect the input imagewithin the minimum processing time required