A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
Information Retrieval
CNSR '04 Proceedings of the Second Annual Conference on Communication Networks and Services Research
A LVQ-based neural network anti-spam email approach
ACM SIGOPS Operating Systems Review
Adaptive Spam Filtering Using Dynamic Feature Space
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Image Analysis for Efficient Categorization of Image-based Spam E-mail
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
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Image based spam email can easily circumvent widely used text based spam email filters. More and more spammers are adapting the technology. Being able to detect the nature of email from its image content is urgently needed. We propose to use OCR (optical character recognition) technology to extract the embedded text from the images and then assess the nature of the email by the extracted text using the same text based engine. This approach avoids maintaining an extra image based detection engine and also takes the benefit of the strong and reasonably mature text based engine. The success of this approach relies on the accuracy of the OCR. However, regardless of how good an OCR is, misrecognition is unavoidable. Therefore, a Markov model which has the ability to tolerate misspells is also proposed. The solution proposed in this paper can be integrated smoothly into existing spam email filters.