A Systematic Literature Review of Artificial Intelligence Algorithms for Deepfake Detection

The evolution of information technology has positioned multimedia content as a pillar of digital communication, but at the same time, it has opened a gap for serious threats in the form of deepfakes. This highly realistic media manipulation challenges information authenticity, privacy, and cybersecurity, which, for Information Technology professionals, presents both technical and ethical challenges. This Systematic Literature Review (SLR) aims to map the development of Artificial Intelligence based algorithms in deepfake detection. Using the PRISMA methodology on 20 selected primary articles (2021-2025), this study aims to identify trends in the use of AI algorithms for deepfake detection, determine the most effective approaches, and analyze the factors contributing to their effectiveness. The analysis results show a paradigm shift from single models (such as CNN) to hybrid architectures (CNN-LSTM-Transformer) and complex multimodal fusion systems. It was found that hybrid algorithms are the closest approach to best practice due to their ability to handle spatial and temporal dimensions simultaneously. Key contributing factors include hierarchical feature extraction, generative data augmentation, and the integration of Explainable AI (XAI).