# Model Robustness with Text Classification: Semantic-preserving adversarial attacks

arXiv: https://arxiv.org/abs/2008.05536 · HTML: https://mlsanity.com/paper/2008.05536/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

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

- [On Sensitivity of Deep Learning Based Text Classification Algorithms to Practical Input Perturbations](https://mlsanity.com/paper/2201.00318/) (2022)
- [When Surface Form Changes Moderation Decisions: A Paired Study of Code-Mixed Workflow Instability](https://mlsanity.com/paper/2606.05654/) (2026)

## Abstract (arXiv)

We propose algorithms to create adversarial attacks to assess model robustness in text classification problems. They can be used to create white box attacks and black box attacks while at the same time preserving the semantics and syntax of the original text. The attacks cause significant number of flips in white-box setting and same rule based can be used in black-box setting. In a black-box setting, the attacks created are able to reverse decisions of transformer based architectures.