A Review of Deepfake Video Generation Techniques: Architectures, Applications, and Limitations

Ismail ILHAN

Abstract: In recent years, deepfake video generation has evolved rapidly due to advancements in deep neural networks and artificial intelligence. This article reviews the key technologies and methods used to produce synthetic videos, including neural network models such as GANs, CNNs, and RNNs. These synthetic videos can be used as a means of animation and entertainment in movies and stories, which serve constructive purposes such as animation and storytelling, but can also be used for malicious purposes. It examines the architectures, datasets, and animation or replacement techniques employed in popular applications, while also highlighting their performance and inherent limitations. The study organizes these techniques into structured tables and diagrams for clarity and offers a reference point for researchers exploring deepfake creation and detection. It is evident that the misuse of such technologies poses ethical and societal challenges, making this a timely and critical area of inquiry.

Keywords: Deepfake videos, face manipulation, GAN, video synthesis, neural networks

How to Cite?: Ismail ILHAN, "A Review of Deepfake Video Generation Techniques: Architectures, Applications, and Limitations", Volume 14 Issue 7, July 2025, International Journal of Science and Research (IJSR), Pages: 705-713, https://www.ijsr.net/getabstract.php?paperid=SR25709023231, DOI: https://dx.doi.org/10.21275/SR25709023231