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：本研究对攻击脚本$\textit{TraceTarnish}$进行了更严格的评估，该脚本基于对抗性文体计量学原理，旨在实现文本消息的作者身份匿名化。为确保攻击的有效性与实用性，我们采集、处理并分析了Reddit评论（这些评论后续被转化为$\textit{TraceTarnish}$数据），以获取关键洞见。转换后的$\textit{TraceTarnish}$数据通过$\textit{StyloMetrix}$进一步增强，生成文体计量特征——这些特征通过信息增益准则进行筛选，仅保留最具信息量、预测性和判别力的特征。研究结果显示：功能词及功能词类型（$L\_FUNC\_A$ $\&$ $L\_FUNC\_T$）、内容词及内容词类型（$L\_CONT\_A$ $\&$ $L\_CONT\_T$）以及类符-形符比（$ST\_TYPE\_TOKEN\_RATIO\_LEMMAS$）均产生显著的信息增益读数。所识别的文体计量线索——功能词频率、内容词分布和类符-形符比——可作为可靠的入侵指标，揭示文本是否经过刻意篡改以掩盖真实作者。同样，这些特征也可作为取证信标，警示防御者对抗性文体计量攻击的存在；当然，若缺乏原始消息对比，此类信号可能难以察觉，因其似乎依赖于文本转换前后的比对分析。“试图抹除痕迹时，往往留下更深的印记。”基于此认知，我们围绕这五个独立特征构建了$\textit{TraceTarnish}$的操作与输出框架，并以此为基础设计并实施了增强方案，进一步强化了攻击效能。