# CRYPTEXT: Database and Interactive Toolkit of Human-Written Text Perturbations in the Wild

arXiv: https://arxiv.org/abs/2301.06494 · HTML: https://mlsanity.com/paper/2301.06494/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 9 references that are in the map.
- First posted on arXiv 16 Jan 2023.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [TextBugger: Generating Adversarial Text Against Real-world Applications](https://mlsanity.com/paper/1812.05271/) (2018)
- [Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers](https://mlsanity.com/paper/1801.04354/) (2018)
- [Exploring Hate Speech Detection in Multimodal Publications](https://mlsanity.com/paper/1910.03814/) (2019)
- [Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems](https://mlsanity.com/paper/1903.11508/) (2019)
- [All-in-one: Multi-task Learning for Rumour Verification](https://mlsanity.com/paper/1806.03713/) (2018)

## Built on by (most cited first, 6 of 6)

- [Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations](https://mlsanity.com/paper/2404.13948/) (2024)
- [Segment Anyword: Mask Prompt Inversion for Open-Set Grounded Segmentation](https://mlsanity.com/paper/2505.17994/) (2025)
- [NoisyHate: Mining Online Human-Written Perturbations for Realistic Robustness Benchmarking of Content Moderation Models](https://mlsanity.com/paper/2303.10430/) (2023)
- [PHISH in MESH: Korean Adversarial Phonetic Substitution and Phonetic-Semantic Feature Integration Defense](https://mlsanity.com/paper/2505.21380/) (2025)
- [WATCHED: A Web AI Agent Tool for Combating Hate Speech by Expanding Data](https://mlsanity.com/paper/2509.01379/) (2025)
- [PEACE 2.0: Grounded Explanations and Counter-Speech for Combating Hate Expressions](https://mlsanity.com/paper/2602.17467/) (2026)

## Abstract (arXiv)

User-generated textual contents on the Internet are often noisy, erroneous, and not in correct forms in grammar. In fact, some online users choose to express their opinions online through carefully perturbed texts, especially in controversial topics (e.g., politics, vaccine mandate) or abusive contexts (e.g., cyberbullying, hate-speech). However, to the best of our knowledge, there is no framework that explores these online ``human-written" perturbations (as opposed to algorithm-generated perturbations). Therefore, we introduce an interactive system called CRYPTEXT. CRYPTEXT is a data-intensive application that provides the users with a database and several tools to extract and interact with human-written perturbations. Specifically, CRYPTEXT helps look up, perturb, and normalize (i.e., de-perturb) texts. CRYPTEXT also provides an interactive interface to monitor and analyze text perturbations online. A short demo video is available at: https://youtu.be/8WT3G8xjIoI