Protecting the anonymity of authors has become a difficult task given the
rise of automated authorship attributors. These attributors are capable of
attributing the author of a text amongst a pool of authors with great accuracy.
In order to counter the rise of these automated attributors, there has also
been a rise of automated obfuscators. These obfuscators are capable of taking
some text, perturbing the text in some manner, and, if successful, deceive an
automated attributor in misattributing the wrong author. We devised three novel
authorship obfuscation methods that utilized a Psycho-linguistic theory known
as Uniform Information Density (UID) theory. This theory states that humans
evenly distribute information amongst speech or text so as to maximize
efficiency. Utilizing this theory in our three obfuscation methods, we
attempted to see how successfully we could deceive two separate attributors.
Obfuscating 50 human and 50 GPT-3 generated articles from the TuringBench
dataset, we observed how well each method did on deceiving the attributors.
While the quality of the obfuscation in terms of semantic preservation and
sensical changes was high, we were not able to find any evidence to indicate
UID was a viable guiding metric for obfuscation. However, due to restrictions
in time we were unable to test a large enough sample of article or tune the
parameters for our attributors to comment conclusively on UID in obfuscation.

在这篇论文中，我们通过利用统一信息密度（UID）理论，设计了三种小说的作者模糊化方法，并通过对 50 篇人工文章和 50 篇 GPT-3 生成文章进行模糊化，观察了每种方法对误导作者识别器的效果，虽然在语义保留和意义变化方面模糊化的质量较高，但我们无法找到任何证据表明 UID 是一种可行的模糊化指标。然而，由于时限限制，我们无法测试足够多的文章样本或调整我们的作者识别器参数，以对 UID 在模糊化中作出最后结论。