Abstract

Deepfake technology has rapidly evolved with advances in deep learning and generative models. To generate highly convincing synthetic media many advanced artificial intelligence (AI) technologies including deep learning, generative AI, auto encoders, and diffusion models are used. While AI tools and technologies can revolutionize virtual reality and digital content production, they also represent serious risks in terms of creating false information or media, identity theft, or media manipulation therefore, developing deepfake detection methods has become an important field of study within the fields of computer vision and multimedia forensics. This paper presents a comprehensive review of deepfake detection research from 2018 to 2025. The methods discussed in this work reference spatial domain and frequency domain techniques, biological signals, transformer-based models, and hybrids of the mentioned detection techniques. A wide range of benchmark datasets, detection techniques, and evaluation metrics are provided.

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