# A Mutation-based Text Generation for Adversarial Machine Learning Applications

arXiv: https://arxiv.org/abs/2212.11808 · HTML: https://mlsanity.com/paper/2212.11808/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 50 references that are in the map.
- First posted on arXiv 21 Dec 2022.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [Generating Natural Language Adversarial Examples](https://mlsanity.com/paper/1804.07998/) (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)
- [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://mlsanity.com/paper/1804.06059/) (2018)
- [Professor Forcing: A New Algorithm for Training Recurrent Networks](https://mlsanity.com/paper/1610.09038/) (2016)
- [Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems](https://mlsanity.com/paper/1903.11508/) (2019)
- [Adversarial Ranking for Language Generation](https://mlsanity.com/paper/1705.11001/) (2017)
- [GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution](https://mlsanity.com/paper/1611.04051/) (2016)
- [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks](https://mlsanity.com/paper/1702.07983/) (2017)
- [Attacking Neural Text Detectors](https://mlsanity.com/paper/2002.11768/) (2020)
- [Adversarial Text Generation via Feature-Mover's Distance](https://mlsanity.com/paper/1809.06297/) (2018)
- [FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments](https://mlsanity.com/paper/1909.10480/) (2019)
- [CSCL: Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2008.10464/) (2020)
- [Meta-CoTGAN: A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation](https://mlsanity.com/paper/2003.11530/) (2020)
- [Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model](https://mlsanity.com/paper/1909.07873/) (2019)
- [Contrastive Cross-Modal Pre-Training: A General Strategy for Small Sample Medical Imaging](https://mlsanity.com/paper/2010.03060/) (2020)
- [Data-Driven Mitigation of Adversarial Text Perturbation](https://mlsanity.com/paper/2202.09483/) (2022)
- [Dynamic Feature Alignment for Semi-supervised Domain Adaptation](https://mlsanity.com/paper/2110.09641/) (2021)
- [Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack](https://mlsanity.com/paper/2011.12720/) (2020)

## Built on by (most cited first, 1 of 1)

- [Diversity Boosts AI-Generated Text Detection](https://mlsanity.com/paper/2509.18880/) (2025)

## Abstract (arXiv)

Many natural language related applications involve text generation, created by humans or machines. While in many of those applications machines support humans, yet in few others, (e.g. adversarial machine learning, social bots and trolls) machines try to impersonate humans. In this scope, we proposed and evaluated several mutation-based text generation approaches. Unlike machine-based generated text, mutation-based generated text needs human text samples as inputs. We showed examples of mutation operators but this work can be extended in many aspects such as proposing new text-based mutation operators based on the nature of the application.