Evaluation of spam detection and prevention frameworks for email and image spam: a state of art
Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services
Improved spam filtering by extraction of information from text embedded image e-mail
Proceedings of the 2009 ACM symposium on Applied Computing
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
Semi Supervised Image Spam Hunter: A Regularized Discriminant EM Approach
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
New filtering approaches for phishing email
Journal of Computer Security - EU-Funded ICT Research on Trust and Security
A survey and experimental evaluation of image spam filtering techniques
Pattern Recognition Letters
SOCIAL: self-organizing classifier ensemble for adversarial learning
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
A survey of image spamming and filtering techniques
Artificial Intelligence Review
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We address the problem of recognizing the so-called image spam, which consists in embedding the spam message into attached images to defeat techniques based on the analysis of e-mails' body text, and in using content obscuring techniques to defeat OCR tools. We propose an approach to recognize image spam based on detecting the presence of content obscuring techniques, and describe a possible implementation based on two low-level image features aimed at detecting obscuring techniques whose consequence is to compromise the OCR effectiveness resulting in character breaking or merging, or in the presence of noise interfering with characters in the binarized image. A preliminary experimental investigation of this approach is reported on a personal data set of spam images.