Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution
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Abstract (arXiv)
In what way could a data breach involving government-issued IDs such as passports, driver's licenses, etc., rival a random voluntary disclosure on a nondescript social-media platform? At first glance, the former appears more significant, and that is a valid assessment. The disclosed data could contain an individual's date of birth and address; for all intents and purposes, a leak of that data would be disastrous. Given the threat, the latter scenario involving an innocuous online post seems comparatively harmless -- or does it? From that post and others like it, a forensic linguist could stylometrically uncover equivalent pieces of information, estimating an age range for the author (adolescent or adult) and narrowing down their geographical location (specific country). While not an exact science -- the determinations are statistical -- stylometry can reveal comparable, though noticeably diluted, information about an individual. To prevent an ID from being breached, simply sharing it as little as possible suffices. Preventing the leakage of personal information from written text requires a more complex solution: adversarial stylometry. In this paper, we explore how performing homoglyph substitution -- the replacement of characters with visually similar alternatives (e.g., "h" texttt[U+0068] rightarrow "h" texttt[U+04BB]) -- on text can degrade stylometric systems.
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References · 14
- 2024RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectorscited by 157
- 2024Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challengescited by 46
- 2022Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspectivecited by 40
- 2024Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attackscited by 25
- 2025Data Poisoning in Deep Learning: A Surveycited by 20
- 2024Authorship Obfuscation in Multilingual Machine-Generated Text Detectioncited by 19
- 2026Large-scale online deanonymization with LLMscited by 13
- 2024SilverSpeak: Evading AI-Generated Text Detectors using Homoglyphscited by 11
- 2022Data-Driven Mitigation of Adversarial Text Perturbationcited by 5
- 2025DAMASHA: Detecting AI in Mixed Adversarial Texts via Segmentation with Human-interpretable Attributioncited by 5
- 2025Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attributioncited by 3
- 2025Tuning for TraceTarnish: Techniques, Trends, and Testing Tangible Traitscited by 3
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