In this study, we more rigorously evaluated our attack script $\textit{TraceTarnish}$, 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 $\textit{TraceTarnish}$ data -- to gain valuable insights. The transformed $\textit{TraceTarnish}$ data was then further augmented by $\textit{StyloMetrix}$ 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 ($L\_FUNC\_A$ $\&$ $L\_FUNC\_T$); content words and content word types ($L\_CONT\_A$ $\&$ $L\_CONT\_T$); and the Type-Token Ratio ($ST\_TYPE\_TOKEN\_RATIO\_LEMMAS$) 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 $\textit{TraceTarnish}$'s operations and outputs around these five isolated features, using them to conceptualize and implement enhancements that further strengthen the attack.

翻译：在本研究中，我们更严格地评估了攻击脚本$\textit{TraceTarnish}$，该脚本利用对抗性文体计量学原理对基于文本的消息的作者身份进行匿名化处理。为确保攻击的有效性和实用性，我们获取、处理并分析了Reddit评论——这些评论随后被转化为$\textit{TraceTarnish}$数据——以获取有价值的见解。转化后的$\textit{TraceTarnish}$数据进一步通过$\textit{StyloMetrix}$进行增强，以生成文体计量特征——这些特征利用信息增益准则进行筛选，仅保留最具信息量、预测性和区分性的特征。我们的结果发现，功能词与功能词类型（$L\_FUNC\_A$与$L\_FUNC\_T$）、实义词与实义词类型（$L\_CONT\_A$与$L\_CONT\_T$）以及类符-形符比（$ST\_TYPE\_TOKEN\_RATIO\_LEMMAS$）均产生了显著的信息增益读数。识别出的文体计量线索——功能词频率、实义词分布以及类符-形符比——可作为可靠的失陷指标（IoCs），揭示文本何时被故意篡改以掩盖其真实作者。同样，这些特征也可充当法医信标，提醒防御者存在对抗性文体计量攻击；然而，在原始消息缺失的情况下，该信号可能很大程度上未被察觉，因为它似乎依赖于变换前后的比较。“试图抹去一个痕迹时，你往往留下一个更大的痕迹。”基于这一理解，我们围绕这五个独立特征构建了$\textit{TraceTarnish}$的操作与输出，并利用它们来概念化与实施进一步增强攻击效果的改进方案。