Deepfake Detection Using Artificial Intelligence

The proliferation of synthetic media generated through deep learning architectures — commonly termed deepfakes — constitutes a growing threat to information integrity, personal security, and democratic processes. This research investigates the effectiveness of artificial intelligence-driven frameworks in detecting deepfake content across visual and multimodal domains. Employing a descriptive-analytical design, primary data were systematically collected from 120 respondents comprising cybersecurity professionals, AI engineers, academic researchers, and media forensics specialists through a structured Likert-scale survey instrument. Three hypotheses were formulated and tested using one-sample t-tests at a 95% confidence level. Results demonstrate that AI-based ensemble models — particularly those combining Vision Transformer and CNN architectures — achieved an F1-score of 94.9%, significantly outperforming single-architecture baselines. Findings further confirm strong professional consensus on the effectiveness of detection technologies and a statistically significant relationship between public awareness and adoption intent. The paper concludes with policy recommendations, investment priorities, and directions for future research in adversarial deepfake mitigation.