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Image spam analysis and detection

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Abstract

Image spam is unsolicited bulk email, where the message is embedded in an image. Spammers use such images to evade text-based filters. In this research, we analyze and compare two methods for detecting spam images. First, we consider principal component analysis (PCA), where we determine eigenvectors corresponding to a set of spam images and compute scores by projecting images onto the resulting eigenspace. The second approach focuses on the extraction of a broad set of image features and selection of an optimal subset using support vector machines (SVM). Both of these detection strategies provide high accuracy with low computational complexity. Further, we develop a new spam image dataset that cannot be detected using our PCA or SVM approach. This new dataset should prove valuable for improving image spam detection capabilities.

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Authors and Affiliations

  1. Department of Computer Science, San Jose State University, San Jose, USA

    Annapurna Annadatha & Mark Stamp

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  1. Annapurna Annadatha
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  2. Mark Stamp
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Correspondence to Mark Stamp.

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Annadatha, A., Stamp, M. Image spam analysis and detection. J Comput Virol Hack Tech 14, 39–52 (2018). https://doi.org/10.1007/s11416-016-0287-x

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