Text Analysis in Adversarial Settings

Author:

Gröndahl Tommi1ORCID,Asokan N.1

Affiliation:

1. Aalto University, Espoo, Finland

Abstract

Textual deception constitutes a major problem for online security. Many studies have argued that deceptiveness leaves traces in writing style, which could be detected using text classification techniques. By conducting an extensive literature review of existing empirical work, we demonstrate that while certain linguistic features have been indicative of deception in certain corpora, they fail to generalize across divergent semantic domains. We suggest that deceptiveness as such leaves no content-invariant stylistic trace , and textual similarity measures provide a superior means of classifying texts as potentially deceptive. Additionally, we discuss forms of deception beyond semantic content, focusing on hiding author identity by writing style obfuscation . Surveying the literature on both author identification and obfuscation techniques, we conclude that current style transformation methods fail to achieve reliable obfuscation while simultaneously ensuring semantic faithfulness to the original text. We propose that future work in style transformation should pay particular attention to disallowing semantically drastic changes.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Cited by 24 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Classifying deceptive reviews for the cultural heritage domain: A lexicon-based approach for the Italian language;Expert Systems with Applications;2024-10

2. LLMs for Explainable Few-shot Deception Detection;Proceedings of the 10th ACM International Workshop on Security and Privacy Analytics;2024-06-19

3. Unveiling Deception in Arabic: Optimization of Deceptive Text Detection Across Formal and Informal Genres;IEEE Access;2024

4. Reframing and Broadening Adversarial Stylometry for Academic Integrity;Springer International Handbooks of Education;2024

5. A generalized solution to verify authorship and detect style change in multi-authored documents;Proceedings of the International Conference on Advances in Social Networks Analysis and Mining;2023-11-06

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