Spam Filtering Based On The Analysis Of Text Information Embedded Into Images
The Journal of Machine Learning Research
Image Spam Filtering Using Visual Information
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
A survey of emerging approaches to spam filtering
ACM Computing Surveys (CSUR)
Detecting spammers via aggregated historical data set
NSS'12 Proceedings of the 6th international conference on Network and System Security
A survey of image spamming and filtering techniques
Artificial Intelligence Review
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The increase of image spam, a kind of spam in which the text message is embedded into an attached image to defeat spam filtering techniques, is becoming an increasingly major problem. For nearly a decade, content based filtering using text classification or machine learning has been a major trend of antispam filtering systems. A Key technique being used by spammers is to embed text into image(s) in spam email. In [4], we proposed two levels of ontology spam filters: a first level global ontology filter and a second level user-customized ontology filter. However, that previous system handles only text e-mail and the percentage of attached images is increasing sharply. The contribution of the paper is that we add an image e-mail handling capability to the previous anti-spam filtering system, enhancing the effectiveness of spam filtering.