IDENTIFYING IMAGE SPAM AUTHORSHIP WITH VARIABLE BIN-WIDTH HISTOGRAM-BASED PROJECTIVE CLUSTERING
Song Gao, Chengcui Zhang, Wei Bang ChenAbstract
In this paper we present a two-phase spam image clustering framework. The proposed framework performs a histogram-based projective clustering on visual features in the first phase, followed by a text-based clustering in the second phase. There are several contributions in this study. First, we address the complex nature of spam image obfuscation techniques. Second, a multi-clue framework is developed to profile spam images of common spamming sources which provide evidence for tracking spam gangs. Third, projective clustering eliminates the need to choose among distance metrics for clustering analysis, while systematically exploring subspaces that correspond to clusters.
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