  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.

Computation and Language;Cryptography and Security