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Overview:

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. Their goal is to automatically classify an image directly as being spam or ham. This paper presents features that focuses on simple properties of the image, making classification as fast as possible. Their evaluation shows that they accurately classify spam images in excess of 90% and up to 99% on real world data. Furthermore, they introduce a new feature selection algorithm that selects features for classification based on their speed as well as predictive power.

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Format:PDFSize:704 KB
Date:Jun 2007
Pages:9

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