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Language-model-based detection cascade for efficient classification of image-based spam e-mail

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2 Author(s)
Jen-Hao Hsia ; Dept. of Electr. Eng., Nat. Taiwan Univ., Taipei, Taiwan ; Ming-Syan Chen

A new challenge in the spam email detection is the emergence of image spam, which consists in embedding the advertising messages into attached images to defeat the conventional text-based anti-spam technologies. New techniques are needed to filter these spam messages. In this paper, we proposed a prototype system to automatically classify an image directly as being spam or ham. The proposed method extracts latent topics in image to train a binary classifier for detecting spam images, and achieves more promising detection accuracy than conventional antispam approaches. In addition, a detection cascade is proposed to further reduce the computation overhead of the spam filter. Our algorithm is experimentally evaluated under a public spam image dataset, and shown to significantly improve both the detection accuracy and execution efficiency over the baseline approach.

Published in:

Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on

Date of Conference:

June 28 2009-July 3 2009