Measuring the Impact of Deepfake Disinformation on Academic Integrity Using Machine Learning and Content Analysis

Natarajapillai, Selvam; Dehham, Sabeeha; Minu, Monickarasi; Regin, Zahraa

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Proceedings of International Conference on Applied Innovation in IT · 2026/06/12 · Vol. 14 · Issue 3 · pp. 381–393

Measuring the Impact of Deepfake Disinformation on Academic Integrity Using Machine Learning and Content Analysis

Selvam Natarajapillai, Sabeeha Hamza Dehham, Monickarasi Sivathanu Minu, Zahraa Jameel Ahmed and Rajan Regin

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Abstract

Deepfakes have also been made vulnerable to spreading false information, and their impact goes up to academic integrity. The article provides an estimate of the impact of the false information deepfakes are spreading on academic research integrity, online tests, and study resources. In the research analysis, it has been kept in mind that deepfakes are most likely being used to spread false information, impersonate academics or educators, and produce copycat scholarly reports. Drawing on examples of deepfake scholarship deception cases, this study focuses on scholarly transparency and confidence metrics. The research will employ academic journals and university databases and deepfake recognition information. The research uses machine learning models as well as content analysis tools to measure the effects of deepfake deception on academic honesty. The study can provide meaningful information about the dangers of deepfakes. It offers institutional solutions to countering this risk such as the installation of detection software, more intensive content verification, and sensitisation of the students and faculty on the dangers of disinformation.

Keywords

False Information Academic Integrity Study Resources Deepfake Scholarship Deepfake Recognition Data Machine Learning Risks of Disinformation.

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