Improving Image Spam Filtering Using Image Text Features

In this paper we consider the approach to image spam filtering based on using image classifiers aimed at discriminating between ham and spam images, previously proposed by other authors. In previous works this approach was implemented using "generic" image features. In this paper we show that its effectiveness can be improved by using specific features related to the graphical characteristics of embedded text. The features we consider are derived from measures which were proposed in our previous works with the aim of detecting image obfuscation techniques often used by spammers to make OCR tools ineffective. An experimental investigation is carried out on a set of images taken from two corpora of real ham and spam emails.

• Type: Conference
• Year: 2008
• Where: CEAS
• Authors: Giorgio Fumera, Fabio Roli, Battista Biggio, Ignazio Pillai
• Keywords: CEAS 2008, Image Classifiers, Image Spam, Images, Internet Technology