Advances in Deepfake Detection: Technologies, Methods and Future Directions

This paper presents a comprehensive survey of deepfake detection technologies, exploring traditional machine learning, deep learning, and preprocessing techniques. The review evaluates the performance of various neural network-based detection models such as MesoNet, Xception, and CapsuleNet while considering classical CNN architectures like DenseNet and ResNet. Additionally, preprocessing methods such as YCbCr color space transformation and edge detection are examined to improve detection accuracy. Even with the fast development of generative adversarial networks (GANs). Existing detection methods suffer such as generalization, dataset limitation and deployment. The focus of this study is the necessity of strong deepfake detection methods and some possible solutions, such as hybrid approaches and increasing diversity of dataset. This survey addresses the critical challenge in the deepfake detection and meanwhile added towards more robust and trustworthy media authentication technique.