  Authorship Attribution (AA) and Authorship Obfuscation (AO) are two competing
tasks of increasing importance in privacy research. Modern AA leverages an
author's consistent writing style to match a text to its author using an AA
classifier. AO is the corresponding adversarial task, aiming to modify a text
in such a way that its semantics are preserved, yet an AA model cannot
correctly infer its authorship. To address privacy concerns raised by
state-of-the-art (SOTA) AA methods, new AO methods have been proposed but
remain largely impractical to use due to their prohibitively slow training and
obfuscation speed, often taking hours. To this challenge, we propose a
practical AO method, ALISON, that (1) dramatically reduces training/obfuscation
time, demonstrating more than 10x faster obfuscation than SOTA AO methods, (2)
achieves better obfuscation success through attacking three transformer-based
AA methods on two benchmark datasets, typically performing 15% better than
competing methods, (3) does not require direct signals from a target AA
classifier during obfuscation, and (4) utilizes unique stylometric features,
allowing sound model interpretation for explainable obfuscation. We also
demonstrate that ALISON can effectively prevent four SOTA AA methods from
accurately determining the authorship of ChatGPT-generated texts, all while
minimally changing the original text semantics. To ensure the reproducibility
of our findings, our code and data are available at:
https://github.com/EricX003/ALISON.
