# Visual Spoofing in content based spam detection

arXiv: https://arxiv.org/abs/2004.05265 · HTML: https://mlsanity.com/paper/2004.05265/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [MockingBERT: A Method for Retroactively Adding Resilience to NLP Models](https://mlsanity.com/paper/2208.09915/) (2022)
- [Homoglyph-based Adversarial Perturbation of Introductory Computer Science Theory Problems](https://mlsanity.com/paper/2605.16286/) (2026)

## Abstract (arXiv)

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.