All You Need is "Leet": Evading Hate-speech Detection AI
arXiv:2505.16263
Abstract
Social media and online forums are increasingly becoming popular. Unfortunately, these platforms are being used for spreading hate speech. In this paper, we design black-box techniques to protect users from hate-speech on online platforms by generating perturbations that can fool state of the art deep learning based hate speech detection models thereby decreasing their efficiency. We also ensure a minimal change in the original meaning of hate-speech. Our best perturbation attack is successfully able to evade hate-speech detection for 86.8 % of hateful text.
10 pages, 22 figures, The source code and data used in this work is available at: https://github.com/SampannaKahu/all_you_need_is_leet