# A Girl Has A Name, And It's ... Adversarial Authorship Attribution for Deobfuscation

arXiv: https://arxiv.org/abs/2203.11849 · HTML: https://mlsanity.com/paper/2203.11849/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [TextBugger: Generating Adversarial Text Against Real-world Applications](https://mlsanity.com/paper/1812.05271/) (2018)
- [Adversarial Training Methods for Semi-Supervised Text Classification](https://mlsanity.com/paper/1605.07725/) (2016)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [The Space of Transferable Adversarial Examples](https://mlsanity.com/paper/1704.03453/) (2017)
- [Efficient Adversarial Training with Transferable Adversarial Examples](https://mlsanity.com/paper/1912.11969/) (2019)
- [Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!](https://mlsanity.com/paper/2103.10013/) (2021)
- [A4NT: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation](https://mlsanity.com/paper/1711.01921/) (2017)
- [ER-AE: Differentially Private Text Generation for Authorship Anonymization](https://mlsanity.com/paper/1907.08736/) (2019)
- [Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution](https://mlsanity.com/paper/1609.06686/) (2016)
- [The Case for Being Average: A Mediocrity Approach to Style Masking and Author Obfuscation](https://mlsanity.com/paper/1707.03736/) (2017)
- [Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness](https://mlsanity.com/paper/2109.04624/) (2021)
- [Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification](https://mlsanity.com/paper/2007.15072/) (2020)
- [Fighting Authorship Linkability with Crowdsourcing](https://mlsanity.com/paper/1405.4918/) (2014)
- [A Girl Has A Name: Detecting Authorship Obfuscation](https://mlsanity.com/paper/2005.00702/) (2020)

## Built on by (most cited first, 3 of 3)

- [Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attribution](https://mlsanity.com/paper/2508.15840/) (2025)
- [Defending Against Authorship Identification Attacks](https://mlsanity.com/paper/2310.01568/) (2023)
- [Forging the Forger: An Attempt to Improve Authorship Verification via Data Augmentation](https://mlsanity.com/paper/2403.11265/) (2024)

## Abstract (arXiv)

Recent advances in natural language processing have enabled powerful privacy-invasive authorship attribution. To counter authorship attribution, researchers have proposed a variety of rule-based and learning-based text obfuscation approaches. However, existing authorship obfuscation approaches do not consider the adversarial threat model. Specifically, they are not evaluated against adversarially trained authorship attributors that are aware of potential obfuscation. To fill this gap, we investigate the problem of adversarial authorship attribution for deobfuscation. We show that adversarially trained authorship attributors are able to degrade the effectiveness of existing obfuscators from 20-30% to 5-10%. We also evaluate the effectiveness of adversarial training when the attributor makes incorrect assumptions about whether and which obfuscator was used. While there is a a clear degradation in attribution accuracy, it is noteworthy that this degradation is still at or above the attribution accuracy of the attributor that is not adversarially trained at all. Our results underline the need for stronger obfuscation approaches that are resistant to deobfuscation