Breaking the Imitation Game: Can LLMs Fool Humans and Machines Alike?
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| Title: | Breaking the Imitation Game: Can LLMs Fool Humans and Machines Alike? |
|---|---|
| Authors: | Ullah, Ubaid1 (AUTHOR) ubaid.ullah@imtlucca.it, Laudanna, Sonia2 (AUTHOR), Di Sorbo, Andrea2 (AUTHOR), Visaggio, Corrado Aaron3 (AUTHOR), Murray, Richard (AUTHOR) rmurray@wiley.com |
| Source: | International Journal of Intelligent Systems. 4/29/2026, Vol. 2026, p1-22. 22p. |
| Subjects: | Stylometry, Impersonation, Disinformation, Classification algorithms, Language models, Natural language processing |
| Abstract: | The emergence of large language models (LLMs) has significantly advanced natural language processing (NLP); however, their capacity to generate human‐like content introduces serious security concerns. In particular, the misuse of LLMs for disinformation and impersonation on social media platforms such as X creates new opportunities for large‐scale manipulation and deception of users. This study aims to conduct a comprehensive investigation to (i) understand the distinct stylistic features effectively mimicked by 10 different LLMs and (ii) distinguish between LLM‐driven and human authors when LLMs are explicitly instructed to mimic a specific human writing style. In particular, we design adversarial prompts to mimic the writing style of human authors based on key stylometric features (quantitative analysis of writing style) and assess the mimicking effectiveness of different LLMs through extensive statistical testing. In addition, we conduct a survey that gauges human ability to recognize the author of a text and train machine learning models to identify human‐ and LLM‐driven authors, focusing on scenarios where specifically crafted adversarial prompts are employed to facilitate style impersonation. Our findings demonstrate that, when explicitly instructed, LLMs can effectively replicate features of human writing style. In addition, the survey results indicate that it is challenging for the participants to distinguish between the different types of authors. In fact, the participants only demonstrated a classification accuracy of 15% in correctly identifying the text generated by the LLM. In contrast, high detection performance is achieved only when the training data incorporate adversarially generated LLM samples produced using impersonation‐oriented prompts. Under this threat‐model–aligned training regime, stylometric‐based classifiers exhibit strong discriminative capability, attaining classification accuracy of up to 99% in distinguishing human‐authored text from LLM‐generated authorship. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193365089 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Breaking the Imitation Game: Can LLMs Fool Humans and Machines Alike? – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ullah%2C+Ubaid%22">Ullah, Ubaid</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ubaid.ullah@imtlucca.it</i><br /><searchLink fieldCode="AR" term="%22Laudanna%2C+Sonia%22">Laudanna, Sonia</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Di+Sorbo%2C+Andrea%22">Di Sorbo, Andrea</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Visaggio%2C+Corrado+Aaron%22">Visaggio, Corrado Aaron</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Murray%2C+Richard%22">Murray, Richard</searchLink> (AUTHOR)<i> rmurray@wiley.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Intelligent+Systems%22">International Journal of Intelligent Systems</searchLink>. 4/29/2026, Vol. 2026, p1-22. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Stylometry%22">Stylometry</searchLink><br /><searchLink fieldCode="DE" term="%22Impersonation%22">Impersonation</searchLink><br /><searchLink fieldCode="DE" term="%22Disinformation%22">Disinformation</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The emergence of large language models (LLMs) has significantly advanced natural language processing (NLP); however, their capacity to generate human‐like content introduces serious security concerns. In particular, the misuse of LLMs for disinformation and impersonation on social media platforms such as X creates new opportunities for large‐scale manipulation and deception of users. This study aims to conduct a comprehensive investigation to (i) understand the distinct stylistic features effectively mimicked by 10 different LLMs and (ii) distinguish between LLM‐driven and human authors when LLMs are explicitly instructed to mimic a specific human writing style. In particular, we design adversarial prompts to mimic the writing style of human authors based on key stylometric features (quantitative analysis of writing style) and assess the mimicking effectiveness of different LLMs through extensive statistical testing. In addition, we conduct a survey that gauges human ability to recognize the author of a text and train machine learning models to identify human‐ and LLM‐driven authors, focusing on scenarios where specifically crafted adversarial prompts are employed to facilitate style impersonation. Our findings demonstrate that, when explicitly instructed, LLMs can effectively replicate features of human writing style. In addition, the survey results indicate that it is challenging for the participants to distinguish between the different types of authors. In fact, the participants only demonstrated a classification accuracy of 15% in correctly identifying the text generated by the LLM. In contrast, high detection performance is achieved only when the training data incorporate adversarially generated LLM samples produced using impersonation‐oriented prompts. Under this threat‐model–aligned training regime, stylometric‐based classifiers exhibit strong discriminative capability, attaining classification accuracy of up to 99% in distinguishing human‐authored text from LLM‐generated authorship. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/int/8117975 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Stylometry Type: general – SubjectFull: Impersonation Type: general – SubjectFull: Disinformation Type: general – SubjectFull: Classification algorithms Type: general – SubjectFull: Language models Type: general – SubjectFull: Natural language processing Type: general Titles: – TitleFull: Breaking the Imitation Game: Can LLMs Fool Humans and Machines Alike? Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ullah, Ubaid – PersonEntity: Name: NameFull: Laudanna, Sonia – PersonEntity: Name: NameFull: Di Sorbo, Andrea – PersonEntity: Name: NameFull: Visaggio, Corrado Aaron – PersonEntity: Name: NameFull: Murray, Richard IsPartOfRelationships: – BibEntity: Dates: – D: 29 M: 04 Text: 4/29/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08848173 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: International Journal of Intelligent Systems Type: main |
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