Techniques and Methodologies for the Detection of DeepFake: A Review

Abstract

Deepfake technology has established itself as a powerful tool for generating highly realistic synthetic media, including manipulated images, videos, and speech. However, the misuse of this technology raises serious concerns related to misinformation, identity theft, and harm to society. As deep-fake content becomes more sophisticated, traditional visual inspection by humans is no longer sufficient for reliable detection. In this study, we perform a comparative evaluation of the main deepfake generation techniques and analyze their influence on the performance of existing detection methods. The research aims to identify which deepfake techniques are most effective at evading detection and assess their potential impact on industrial and real-world applications. Based on this analysis, we discuss key challenges and propose future research directions to improve deepfake detection frameworks.