Image spam analysis and detection
Publication Date
February 2018
Document Type
Article
Publication Title
Journal of Computer Virology and Hacking Techniques
Volume
14
Issue
1
DOI
10.1007/s11416-016-0287-x
First Page
39
Last Page
52
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.
Recommended Citation
Mark Stamp and Annapurna Annadatha. "Image spam analysis and detection" Journal of Computer Virology and Hacking Techniques (2018): 39-52. https://doi.org/10.1007/s11416-016-0287-x
