# StegoStylo: Squelching Stylometric Scrutiny through Steganographic Stitching

arXiv: https://arxiv.org/abs/2601.09056 · HTML: https://mlsanity.com/paper/2601.09056/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 14 Jan 2026.

## Builds on (most cited first, 3 of 3)

- [Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attribution](https://mlsanity.com/paper/2508.15840/) (2025)
- [Tuning for TraceTarnish: Techniques, Trends, and Testing Tangible Traits](https://mlsanity.com/paper/2512.03465/) (2025)
- [Stylometry Analysis of Human and Machine Text for Academic Integrity](https://mlsanity.com/paper/2601.01225/) (2026)

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

- [Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution](https://mlsanity.com/paper/2604.10271/) (2026)
- [Occluded Oculus: Operationalizing Stylistic Obscurement](https://mlsanity.com/paper/2607.24411/) (2026)

## Abstract (arXiv)

Stylometry -- the identification of an author through analysis of a text's style (i.e., authorship attribution) -- serves many constructive purposes: it supports copyright and plagiarism investigations, aids detection of harmful content, offers exploratory cues for certain medical conditions (e.g., early signs of dementia or depression), provides historical context for literary works, and helps uncover misinformation and disinformation. In contrast, when stylometry is employed as a tool for authorship verification -- confirming whether a text truly originates from a claimed author -- it can also be weaponized for malicious purposes. Techniques such as de-anonymization, re-identification, tracking, profiling, and downstream effects like censorship illustrate the privacy threats that stylometric analysis can enable. Building on these concerns, this paper further explores how adversarial stylometry combined with steganography can counteract stylometric analysis. We first present enhancements to our adversarial attack, textitTraceTarnish, providing stronger evidence of its capacity to confound stylometric systems and reduce their attribution and verification accuracy. Next, we examine how steganographic embedding can be fine-tuned to mask an author's stylistic fingerprint, quantifying the level of authorship obfuscation achievable as a function of the proportion of words altered with zero-width Unicode characters. Based on our findings, steganographic coverage of 33% or higher seemingly ensures authorship obfuscation. Finally, we reflect on the ways stylometry can be used to undermine privacy and argue for the necessity of defensive tools like textitTraceTarnish.