# A4NT: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation

arXiv: https://arxiv.org/abs/1711.01921 · HTML: https://mlsanity.com/paper/1711.01921/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2017.

## Built on by (most cited first, 30 of 42)

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- [Realtime Robust Malicious Traffic Detection via Frequency Domain Analysis](https://mlsanity.com/paper/2106.14707/) (2021)
- [StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements](https://mlsanity.com/paper/2408.15666/) (2024)
- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
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- [STEER: Unified Style Transfer with Expert Reinforcement](https://mlsanity.com/paper/2311.07167/) (2023)
- [Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling](https://mlsanity.com/paper/2101.11310/) (2021)
- [The Fire Thief Is Also the Keeper: Balancing Usability and Privacy in Prompts](https://mlsanity.com/paper/2406.14318/) (2024)
- [Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness](https://mlsanity.com/paper/2109.04624/) (2021)
- [Fact-Saboteurs: A Taxonomy of Evidence Manipulation Attacks against Fact-Verification Systems](https://mlsanity.com/paper/2209.03755/) (2022)
- [DetoxLLM: A Framework for Detoxification with Explanations](https://mlsanity.com/paper/2402.15951/) (2024)
- [Keep It Private: Unsupervised Privatization of Online Text](https://mlsanity.com/paper/2405.10260/) (2024)
- [Avengers Ensemble! Improving Transferability of Authorship Obfuscation](https://mlsanity.com/paper/2109.07028/) (2021)
- [JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models](https://mlsanity.com/paper/2402.08761/) (2024)
- [Masks and Mimicry: Strategic Obfuscation and Impersonation Attacks on Authorship Verification](https://mlsanity.com/paper/2503.19099/) (2025)
- [From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey](https://mlsanity.com/paper/2110.15871/) (2021)
- [Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations](https://mlsanity.com/paper/2201.06384/) (2022)
- [Impeding LLM-assisted Cheating in Introductory Programming Assignments via Adversarial Perturbation](https://mlsanity.com/paper/2410.09318/) (2024)
- [Preventing Author Profiling through Zero-Shot Multilingual Back-Translation](https://mlsanity.com/paper/2109.09133/) (2021)
- [Style Transfer with Multi-iteration Preference Optimization](https://mlsanity.com/paper/2406.11581/) (2024)
- [A Girl Has A Name, And It's ... Adversarial Authorship Attribution for Deobfuscation](https://mlsanity.com/paper/2203.11849/) (2022)
- [Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy](https://mlsanity.com/paper/2306.02488/) (2023)
- [Making Translators Privacy-aware on the User's Side](https://mlsanity.com/paper/2312.04068/) (2023)
- [DiDOTS: Knowledge Distillation from Large-Language-Models for Dementia Obfuscation in Transcribed Speech](https://mlsanity.com/paper/2410.04188/) (2024)

## Abstract (arXiv)

Text-based analysis methods allow to reveal privacy relevant author attributes such as gender, age and identify of the text's author. Such methods can compromise the privacy of an anonymous author even when the author tries to remove privacy sensitive content. In this paper, we propose an automatic method, called Adversarial Author Attribute Anonymity Neural Translation (A4NT), to combat such text-based adversaries. We combine sequence-to-sequence language models used in machine translation and generative adversarial networks to obfuscate author attributes. Unlike machine translation techniques which need paired data, our method can be trained on unpaired corpora of text containing different authors. Importantly, we propose and evaluate techniques to impose constraints on our A4NT to preserve the semantics of the input text. A4NT learns to make minimal changes to the input text to successfully fool author attribute classifiers, while aiming to maintain the meaning of the input. We show through experiments on two different datasets and three settings that our proposed method is effective in fooling the author attribute classifiers and thereby improving the anonymity of authors.