Image Spam Filtering by Content Obscuring Detection

  title={Image Spam Filtering by Content Obscuring Detection},
  author={Battista Biggio and Giorgio Fumera and Ignazio Pillai and Fabio Roli},
We address the problem of filtering image spam, a rapidly spreading kind of spam in which the text message is embedded into attached images to defeat spam filtering techniques based on the analysis of e-mail’s body text. We propose an approach based on low-level image processing techniques to detect one of the main characterstics of most image spam, namely the use of content obscuring techniques to defeat OCR tools. A preliminary experimental evaluation of our approach is reported on a personal… CONTINUE READING
Highly Cited
This paper has 100 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.


Publications citing this paper.
Showing 1-10 of 28 extracted citations

Detecting Image Spam Based on K-Labels Propagation Model

2013 10th Web Information System and Application Conference • 2013
View 5 Excerpts
Highly Influenced

SVM with Gaussian kernel-based image spam detection on textual features

2017 3rd International Conference on Computational Intelligence & Communication Technology (CICT) • 2017
View 1 Excerpt

A modular approach towards image spam filtering using multiple classifiers

2014 IEEE International Conference on Computational Intelligence and Computing Research • 2014
View 1 Excerpt

101 Citations

Citations per Year
Semantic Scholar estimates that this publication has 101 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-10 of 11 references