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.

中文翻译：

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基于词嵌入的作者检测模型对保留语义的对抗性扰动的敏感性

作者分析是自然语言处理领域中的重要主题。它允许检测最有可能撰写文章，新闻，书籍或消息的作者。该技术在与作者身份归属，窃检测，样式分析，错误信息来源等任务相关的用途中有多种用途。本文的重点是探讨已建立的对抗输入操作方法的局限性和敏感性。为此，我们使用那些已建立的技术，首先为作者检测和输入扰动开发了实验框架。接下来，我们通过实验对输入叙述的保留语义的对抗性扰动集合评估了作者身份检测模型的性能。最后，