A Comprehensive Evaluation of Deepfake Detection Methods: Approaches, Challenges and Future Prospects

Advances in technology have made deepfake forgeries easier, posing serious ethical and security risks that highlight the urgent need for better detection methods. This paper provides a comprehensive discussion of various Deepfake detection approaches, including methods based on physical attributes and visual inconsistencies, data-driven techniques (such as spatial and frequency domain detection methods), and those using generative models. Based on the classification and introduction of representative methods, the paper further compares their performance across different datasets, revealing that while current methods can detect deepfake to some extent, they generally suffer from poor generalization and accuracy when dealing with different types of forgeries or low-quality data. In conclusion, this study offers insights into the development of future deepfake detection technologies, emphasizing the importance of combining multiple approaches and improving model generalization to address increasingly complex forgery scenarios. It can serve as a valuable reference for researchers looking to understand the advancements in this field.