# Tuning for TraceTarnish: Techniques, Trends, and Testing Tangible Traits

arXiv: https://arxiv.org/abs/2512.03465 · HTML: https://mlsanity.com/paper/2512.03465/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 3 Dec 2025.

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

- [StyloMetrix: An Open-Source Multilingual Tool for Representing Stylometric Vectors](https://mlsanity.com/paper/2309.12810/) (2023)
- [Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attribution](https://mlsanity.com/paper/2508.15840/) (2025)
- [Authorship identification under domain shift: a survey of stylistic measures and learned author representations](https://mlsanity.com/paper/2310.00436/) (2023)

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

- [StegoStylo: Squelching Stylometric Scrutiny through Steganographic Stitching](https://mlsanity.com/paper/2601.09056/) (2026)
- [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)

In this study, we more rigorously evaluated our attack script textitTraceTarnish, which leverages adversarial stylometry principles to anonymize the authorship of text-based messages. To ensure the efficacy and utility of our attack, we sourced, processed, and analyzed Reddit comments -- comments that were later alchemized into textitTraceTarnish data -- to gain valuable insights. The transformed textitTraceTarnish data was then further augmented by textitStyloMetrix to manufacture stylometric features -- features that were culled using the Information Gain criterion, leaving only the most informative, predictive, and discriminative ones. Our results found that function words and function word types (LFUNCA & LFUNCT); content words and content word types (LCONTA & LCONTT); and the Type-Token Ratio (STTYPETOKENRATIOLEMMAS) yielded significant Information-Gain readings. The identified stylometric cues -- function-word frequencies, content-word distributions, and the Type-Token Ratio -- serve as reliable indicators of compromise (IoCs), revealing when a text has been deliberately altered to mask its true author. Similarly, these features could function as forensic beacons, alerting defenders to the presence of an adversarial stylometry attack; granted, in the absence of the original message, this signal may go largely unnoticed, as it appears to depend on a pre- and post-transformation comparison. "In trying to erase a trace, you often imprint a larger one." Armed with this understanding, we framed textitTraceTarnish's operations and outputs around these five isolated features, using them to conceptualize and implement enhancements that further strengthen the attack.