Conference Paper

A Discriminative Classifier Learning Approach to Image Modeling and Spam Image Identification.

Conference: CEAS 2007 - The Fourth Conference on Email and Anti-Spam, 2-3 August 2007, Mountain View, California, USA
Source: DBLP
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    ABSTRACT: Email spam is a major problem for the sustainability of the Internet and global commerce. Every day million of emails are sent across by spammers to targeted population to advertise products, services, spread dangerous software etc. Currently a number of spam detection algorithms have been proposed in the literature to classify email spam. Most of these algorithms can be categorized as meta-data based, content based, behaviour based, etc. The existing literature has heavily focussed on advancing one or the other kind of approach i.e. there are a lot of algorithms doing content based or behaviour based detection, however there is not much work done that evaluates the effect of applying different algorithms in a step by step and/or iterative manner to achieve optimised classification. This research would aim to evaluate existing algorithms and propose a spam detection framework to automatically choose the correct algorithm sequence to do the classification based upon intelligently identified heuristics from the email profile.
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    ABSTRACT: We propose an unsupervised image clustering framework for revealing the common origins, i.e. the spam gangs, of unsolicited emails. In particular, we target email spam with image attachments because spam information is harder to extract due to information hiding enabled by various image obfuscation techniques. To identify spam gangs, we observe that spam images from the same source are usually composed of visually similar elements which are arranged and altered in many different ways in order to trick the spam filter. We propose to infer spam images originated from the same spam gang by investigating spam email similarity in terms of their visual appearance and editing style. In particular, a data mining technique based on unsupervised image clustering is proposed in this paper to solve this problem. This is achieved by first dividing a spam image into different areas/segments, including texts, foreground graphic illustrations, and background areas. The proposed framework then extracts characteristic visual features from segmented areas, including text layout, visual features of foreground graphic illustrations and its spatial layout, and background texture features. In the clustering stage, all spam images are first categorized as illustrated images and text mainly images according to the existence of foreground illustration objects. Then illustrated images are clustered based on the color and/or foreground layout, while text mainly images are clustered based on the text layouts and/or background textures. A novel unsupervised ranked clustering algorithm is proposed for feature fusion, which is used in combination with the traditional hierarchical clustering algorithm for clustering. We test the proposed approach using different settings and combinations of features and measure the overall performance with V-measure.

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May 23, 2014