Abstract
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, $\textit{TraceTarnish}$, 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 $\textit{TraceTarnish}$.
Abstract (translated)
语体测量法——通过分析文本的风格来识别作者（即，确定作者身份）——在许多建设性方面发挥着重要作用：它支持版权和抄袭调查，有助于检测有害内容，为某些医疗状况提供探索线索（例如痴呆症或抑郁症的早期迹象），为文学作品提供历史背景，并帮助揭露错误信息和虚假信息。然而，当语体测量被用作作者身份验证工具——确认文本是否真正来自声称的作者时——它也可能被用于恶意目的。诸如去匿名化、重新识别、跟踪、个人画像以及由此产生的审查等隐私威胁都可能因语体分析而引发。 基于这些担忧，本文进一步探讨了通过结合对抗性语体测量和隐写术来对抗语体分析的方法。首先，我们介绍了对我们的对抗攻击技术$\textit{TraceTarnish}$的改进，提供了更强有力的证据证明其能够混淆语体系统的识别能力，并降低其归因与验证的准确性。接下来，我们将研究如何通过调整隐写术嵌入以掩盖作者的独特风格特征，量化使用零宽度Unicode字符修改的比例对作者身份模糊化程度的影响。根据我们的研究成果，在33%或更高的隐写覆盖范围内，似乎能够确保有效的作者身份混淆。 最后，我们反思了语体测量如何被用于侵犯隐私，并主张需要防御性工具如$\textit{TraceTarnish}$来保护个人免受此类技术带来的风险。
URL
https://arxiv.org/abs/2601.09056
