A Survey of Deepfake Detection Methods: Innovations, Accuracy, and Future Directions

Abstract

Deepfake technology has emerged as a significant challenge in digital media, posing risks related to misinformation and identity theft. This paper provides a comprehensive review of deepfake detection techniques, highlighting advancements in traditional machine learning, deep learning models, hybrid approaches, and attention mechanisms. We evaluate the effectiveness of various methods based on accuracy, computational efficiency, and practical applicability, using key datasets and benchmarking systems. Our review underscores the progress made in detecting deepfakes and identifies areas for future research, including real-time detection, multimodal approaches, and improvements in computational efficiency. Key Words: Deepfake detection, machine learning, deep learning, convolutional neural networks, transformers, attention mechanisms, multimodal data, benchmarking systems, datasets.