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：本研究以更高严谨性评估了攻击脚本$\textit{TraceTarnish}$，该脚本利用对抗性风格学原理实现文本消息作者身份的匿名化。为确保攻击的有效性与实用性，我们采集、处理并分析了Reddit评论数据——这些评论随后被转化为$\textit{TraceTarnish}$数据——以获取关键洞察。经变换的$\textit{TraceTarnish}$数据进一步由$\textit{StyloMetrix}$增强，生成风格学特征；随后基于信息增益准则筛选特征，仅保留最具信息量、预测力与判别力的维度。研究发现，功能词及其类型（$L\_FUNC\_A$与$L\_FUNC\_T$）、内容词及其类型（$L\_CONT\_A$与$L\_CONT\_T$）、以及词例-类型比（$ST\_TYPE\_TOKEN\_RATIO\_LEMMAS$）均呈现出显著的信息增益值。所识别的风格学线索——功能词频次、内容词分布与词例-类型比——可作为可靠的入侵指标（IoCs），揭示文本是否被刻意修改以掩盖真实作者身份。类似地，这些特征可用作取证信标，警示防御者对抗性风格学攻击的存在；然而，若无原始消息作为对照，该信号可能难以被察觉——因其依赖于变换前后的对比分析。"试图抹除痕迹时，往往留下更深的印记。"基于这一认知，我们将$\textit{TraceTarnish}$的操作与输出围绕上述五个分离特征进行重构，并以此构思与实现了增强攻击效果的技术方案。