Deepfake detection models and methods in artificial intelligence and ‎insights from media and social culture perspective

This study explores the phenomenon of deepfakes as a consequence of rapid advancements in artificial intelligence, machine learning, and deep learning technologies over the past decade. The primary objective is to analyze various methods for detecting deepfakes and examine their social and legal implications.

The research categorizes and evaluates four types of deepfake detection

deep learning-based, classical machine learning-based, statistical, and blockchain-based approaches. It also assesses the performance of these methods on different datasets.

The findings indicate that deep learning-based methods are more effective in detecting deepfakes compared to other approaches. Furthermore, the study analyzes the impact of deepfakes from multiple perspectives, including media and society, media production, representation, dissemination, audience, gender, law, and politics. The results reveal that society is currently unprepared to effectively combat deepfakes, due to a combination of technological, educational, and regulatory shortcomings.

Originality/Value: 

This research provides a comprehensive and comparative analysis of deepfake detection methods, offering valuable insights for policymakers and researchers. The study highlights the urgent need for effective strategies to address the rapidly evolving challenges posed by deepfakes in both social and legal contexts.