English

Visual Spoofing in content based spam detection

Cryptography and Security 2020-11-11 v2 Machine Learning Machine Learning

Abstract

Although the problem of spam classification seems to be solved, there are still vulnerabilities in the current spam filters that could be easily exploited. We present one such vulnerability, in which one could replace some characters with corresponding characters from a different alphabet. These characters are visually similar, yet have a different Unicode encoding. With this approach spammers can create messages that bypass existing spam filters. Moreover, we show that this approach can be used to avoid plagiarism detection, and in other applications that use natural language processing for automatic analysis of text documents.

Keywords

Cite

@article{arxiv.2004.05265,
  title  = {Visual Spoofing in content based spam detection},
  author = {Mark Sokolov and Kehinde Olufowobi and Nic Herndon},
  journal= {arXiv preprint arXiv:2004.05265},
  year   = {2020}
}
R2 v1 2026-06-23T14:47:37.810Z