Deepfake Detection: Advances, Challenges, and Future Directions

Deepfake technology, driven by advanced artificial intelligence, generates highly realistic synthetic media, posing significant threats to digital authenticity, secu- rity, and societal trust. This paper provides a comprehensive review of recent ad- vances in deepfake detection, focusing on convolutional neural networks (CNNs), transformer-based architectures, and multi-modal approaches that leverage audio- visual inconsistencies. We evaluate their performance on benchmark datasets such as FaceForensics++, DeepFake Detection Challenge (DFDC), and Celeb-DF, highlighting achievements like 99.73% accuracy by MFF-Net on FaceForensics++. However, challenges such as poor cross-dataset generalization, vulnerability to ad- versarial attacks, and high computational costs persist. We discuss the strengths and limitations of these methods, their real-world applicability, and propose future research directions, including robust detection frameworks, real-time systems, and explainable AI to enhance trust. This study underscores the need for continued innovation to counter evolving deepfake technologies and mitigate their societal impact.