  Authorship analysis is an important subject in the field of natural language
processing. It allows the detection of the most likely writer of articles,
news, books, or messages. This technique has multiple uses in tasks related to
authorship attribution, detection of plagiarism, style analysis, sources of
misinformation, etc. The focus of this paper is to explore the limitations and
sensitiveness of established approaches to adversarial manipulations of inputs.
To this end, and using those established techniques, we first developed an
experimental frame-work for author detection and input perturbations. Next, we
experimentally evaluated the performance of the authorship detection model to a
collection of semantic-preserving adversarial perturbations of input
narratives. Finally, we compare and analyze the effects of different
perturbation strategies, input and model configurations, and the effects of
these on the author detection model.
