Handbook of pattern recognition & computer vision
Making large-scale support vector machine learning practical
Advances in kernel methods
Markets for attention: will postage for email help?
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Unsupervised Image Clustering Using the Information Bottleneck Method
Proceedings of the 24th DAGM Symposium on Pattern Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Spam Filtering Based On The Analysis Of Text Information Embedded Into Images
The Journal of Machine Learning Research
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
Detecting image spam using local invariant features and pyramid match kernel
Proceedings of the 18th international conference on World wide web
Revealing common sources of image spam by unsupervised clustering with visual features
Proceedings of the 2009 ACM symposium on Applied Computing
Semi Supervised Image Spam Hunter: A Regularized Discriminant EM Approach
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Image spam clustering: an unsupervised approach
MiFor '09 Proceedings of the First ACM workshop on Multimedia in forensics
Language-model-based detection cascade for efficient classification of image-based spam e-mail
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
A comprehensive approach to image spam detection: from server to client solution
IEEE Transactions on Information Forensics and Security
Identifying and resolving hidden text salting
IEEE Transactions on Information Forensics and Security
A case for query by image and text content: searching computer help using screenshots and keywords
Proceedings of the 20th international conference on World wide web
Spam detection in online classified advertisements
Proceedings of the 2011 Joint WICOW/AIRWeb Workshop on Web Quality
A survey and experimental evaluation of image spam filtering techniques
Pattern Recognition Letters
Detecting near-duplicate SPITs in voice mailboxes using hashes
ISC'11 Proceedings of the 14th international conference on Information security
BASIL: effective near-duplicate image detection using gene sequence alignment
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
On online high-dimensional spherical data clustering and feature selection
Engineering Applications of Artificial Intelligence
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Email spam is a much studied topic, but even though current email spam detecting software has been gaining a competitive edge against text based email spam, new advances in spam generation have posed a new challenge: image-based spam. Image based spam is email which includes embedded images containing the spam messages, but in binary format. In this paper, we study the characteristics of image spam to propose two solutions for detecting image-based spam, while drawing a comparison with the existing techniques. The first solution, which uses the visual features for classification, offers an accuracy of about 98%, i.e. an improvement of at least 6% compared to existing solutions. SVMs (Support Vector Machines) are used to train classifiers using judiciously decided color, texture and shape features. The second solution offers a novel approach for near duplication detection in images. It involves clustering of image GMMs (Gaussian Mixture Models) based on the Agglomerative Information Bottleneck (AIB) principle, using Jensen-Shannon divergence (JS) as the distance measure.