Learning Fast Classifiers for Image Spam

Recently, spammers have proliferated "image spam", emails which contain the text of the spam message in a human readable image instead of the message body, making detection by conventional content filters difficult. New techniques are needed to filter these messages. Our goal is to automatically classify an image directly as being spam or ham. We present features that focus on simple properties of the image, making classification as fast as possible. Our evaluation shows that they accurately classify spam images in excess of 90% and up to 99% on real world data. Furthermore, we introduce a new feature selection algorithm that selects features for classification based on their speed as well as predictive power. This technique produces an accurate system that runs in a tiny fraction of the time. Finally, we introduce Just in Time (JIT) feature extraction, which creates features at classification time as needed by the classifier. We demonstrate JIT extraction using a JIT decisi...

• Type: Conference
• Year: 2007
• Where: CEAS
• Authors: Mark Dredze, Reuven Gevaryahu, Ari Elias-Bachrach
• Keywords: CEAS 2007, Human Readable Image, Image Spam, Image Spam Classification, Internet Technology