Paper Info
Title
A Nonnegative Sparsity Induced Similarity Measure With Application To Cluster Analysis Of Spam Images
Abstract
Image spam is an email spam that embeds text content into graphical images to bypass traditional spam filters. The majority of previous approaches focus on filtering image spam from client side. To effectively detect the attack activities of the spammers and fast trace back the spam sources, it is also essential to employ cluster analysis to comprehensively filter the image emails on the server side. In this paper, we present a nonnegative sparsity induced similarity measure for cluster analysis of spam images. This similarity measure is based on an assumption that a spam image should be represented well by the nonnegative linear combination of a small number of spam images in the same cluster. It is due to the observation that spammers generate large number of varieties from a single image source with different image processing and manipulation techniques. Experiments on a spam image dataset collected from our department email server demonstrated the advantages of the proposed approach.
Year
DOI
Venue
2010
10.1109/ICASSP.2010.5495246
2010 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
Keywords
Field
DocType
Nonnegative sparse representation, Image spam filtering, Cluster analysis
Bag-of-words model,Data mining,Pattern recognition,Similarity measure,Computer science,Visualization,Image processing,Filter (signal processing),Image spam,Artificial intelligence,Cluster analysis,Email spam
Conference
ISSN
Citations 
PageRank 
1520-6149
7
0.46
References 
Authors
10
3
Name
Order
Citations
PageRank
Yan Gao119710.54
Alok N. Choudhary226024.04
Gang Hua32796157.90