Deepfake & Synthetic Media Detection

Abstract

Deepfake and synthetic media technologies have quickly changed with the growth of artificial intelligence. This has raised serious concerns about misinformation, security, and digital trust. This review paper looks at recent research on deepfake detection in image, video, and audio areas. It studies various methods like Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), MesoNet, and Multilayer Perceptron (MLP) models to see how well they identify manipulated content. The review points out that while existing methods show high accuracy on controlled datasets, they struggle in real-world situations, including issues like compression, noise, and unfamiliar manipulation techniques. Most methods also focus on just one type of media, which limits their strength. The paper highlights crucial gaps, such as the lack of generalization, dependence on specific datasets, and limited integration of different types of media. Finally, it stresses the need for strong and flexible multimodal deepfake detection systems to tackle the growing challenges from synthetic media effectively.