mlsanityData to 14 Oct 2026

Reproduction and Replication of an Adversarial Stylometry Experiment

arXiv 2208.07395 ↗First posted 15 Aug 2022cs.CLcited by 2

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Abstract (arXiv)

Maintaining anonymity in natural language communication remains a challenging task. Even when the number of candidate authors is large, standard authorship attribution techniques that analyze writing style predict the original author with uncomfortably high accuracy. Adversarial stylometry provides a defense against authorship attribution, helping users avoid unwanted deanonymization. This paper reproduces and replicates experiments from a seminal study of defenses against authorship attribution (Brennan et al., 2012). After reproducing the experiment using the original data, we then replicate the experiment by repeating the online field experiment using the procedures described in the original paper. Although we reach the same conclusion as the original paper, our results suggest that the defenses studied may be overstated in their effectiveness. This is largely due to the absence of a control group in the original study. In our replication, we find evidence suggesting that an entirely automatic method, round-trip translation, warrants re-examination because it appears to reduce the effectiveness of established authorship attribution methods.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 12 of its 48 references that are in the map. First posted on arXiv 15 Aug 2022.

Builds on

12 of 48 references.

References · 12

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  5. 2019Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translationcited by 52
  6. 2017A4NT: Author Attribute Anonymity by Adversarial Training of Neural Machine Translationcited by 42
  7. 2019ER-AE: Differentially Private Text Generation for Authorship Anonymizationcited by 35
  8. 2017Evaluating prose style transfer with the Biblecited by 23
  9. 2021The Topic Confusion Task: A Novel Scenario for Authorship Attributioncited by 17
  10. 2018Style Obfuscation by Invariancecited by 10
  11. 2014Fighting Authorship Linkability with Crowdsourcingcited by 8
  12. 2021Preventing Author Profiling through Zero-Shot Multilingual Back-Translationcited by 4

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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.