# A Visual Analytics Framework for Adversarial Text Generation

arXiv: https://arxiv.org/abs/1909.11202 · HTML: https://mlsanity.com/paper/1909.11202/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

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

- [RECAST: Enabling User Recourse and Interpretability of Toxicity Detection Models with Interactive Visualization](https://mlsanity.com/paper/2102.04427/) (2021)
- [ShortcutLens: A Visual Analytics Approach for Exploring Shortcuts in Natural Language Understanding Dataset](https://mlsanity.com/paper/2208.08010/) (2022)

## Abstract (arXiv)

This paper presents a framework which enables a user to more easily make corrections to adversarial texts. While attack algorithms have been demonstrated to automatically build adversaries, changes made by the algorithms can often have poor semantics or syntax. Our framework is designed to facilitate human intervention by aiding users in making corrections. The framework extends existing attack algorithms to work within an evolutionary attack process paired with a visual analytics loop. Using an interactive dashboard a user is able to review the generation process in real time and receive suggestions from the system for edits to be made. The adversaries can be used to both diagnose robustness issues within a single classifier or to compare various classifier options. With the weaknesses identified, the framework can also be used as a first step in mitigating adversarial threats. The framework can be used as part of further research into defense methods in which the adversarial examples are used to evaluate new countermeasures. We demonstrate the framework with a word swapping attack for the task of sentiment classification.