AI and Cybersecurity: Combating Deepfakes and Misinformation in Nepal

Abstract

Background: The rapid expansion of artificial intelligence (AI) and digital communication platforms has intensified concerns regarding deepfakes and misinformation, particularly in developing contexts like Nepal. These challenges pose risks to information integrity, public trust, and digital security, necessitating effective mitigation strategies.

Methods: This study employs a quantitative research design using primary data collected through a structured online questionnaire. The questionnaire was shared with approximately 500 individuals across Nepal, of whom 400 respondents completed the survey and met the study requirements for final analysis, resulting in a response rate of 80%.  A total of 400 responses were obtained using purposive sampling. All variables were measured using a five-point Likert scale. Data were analyzed using SPSS through descriptive statistics, cross-tabulation, Pearson correlation, and multiple linear regression analysis.

Results: The findings reveal that Public Awareness Campaigns and Digital Literacy significantly and positively influence the effectiveness of combating deepfakes and misinformation. Regression analysis indicates that Public Awareness Campaigns (B = 0.476, p < 0.001) and Digital Literacy (B = 0.177, p = 0.008) are significant predictors, while Cybersecurity Infrastructure, Government Policies, and Social Media Platform Measures show non-significant or weak effects. The model is statistically significant (F = 42.983, p < 0.001) and the model explains 35.3% of the variance (R² = 0.353, F = 42.983, p < 0.001).

Conclusion: The study concludes that human-centered factors, particularly awareness and education, are more influential than institutional or technological measures in combating deepfakes and misinformation in Nepal. Strengthening digital literacy and public awareness is essential for improving digital resilience.

Novelty: This study provides empirical evidence from Nepal highlighting the comparative dominance of human-centric factors over institutional mechanisms in addressing AI-driven misinformation challenges.