Unmasking Deepfakes: A Comprehensive Review of Deep Learning-Based Detection Methods

Abstract

Deepfakes, a term combining "deep learning" and "fake," refer to synthetic media where a person's likeness in an image or video is replaced with someone else's. These manipulations present significant ethical, privacy, and security challenges. This comprehensive review explores various deep learning-based methods used to detect deepfakes, highlighting their evolution, strengths, and limitations. We delve into the application of convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and capsule networks (CapsNets) in detecting these forgeries. Key evaluation metrics, notable datasets, and persistent challenges in the field are discussed. The review concludes by identifying future directions in deepfake detection, emphasizing the need for robustness, real-time capabilities, and model explainability to effectively combat the rise of deepfakes.