Image Spam Filtering Using Visual Information

Abstract

We address the problem of recognizing the so-called image spam, which consists in embedding the spam message into attached images to defeat techniques based on the analysis of e-mails' body text, and in using content obscuring techniques to defeat OCR tools. We propose an approach to recognize image spam based on detecting the presence of content obscuring techniques, and describe a possible implementation based on two low-level image features aimed at detecting obscuring techniques whose consequence is to compromise the OCR effectiveness resulting in character breaking or merging, or in the presence of noise interfering with characters in the binarized image. A preliminary experimental investigation of this approach is reported on a personal data set of spam images.