Published Paper Details:

The growth of deepfake technology has raised great concerns about privacy, misinformation and cybersecurity. Advanced AI can make it difficult to say real and false media, as deeper and more visual content can change. In this paper we will explore current methods to recognize and prevent deepfakes and check how well they work and have limitations. It also explains how deeplearning to create faces changes, focusing on Stylegan and how it is used to edit, restore and change faces in different styles.We also look into the famous deepfake tool deepfacelab and sketch it to work with high resolution facial films. Apart from Visual Deepakes, we look at FluentLip, the latest audio conditioned LipenSthesis model that improves synthesis language synchronization and smoothness. Finally, let's look at recent advances in speech production. We present an approach to using emotions to create more natural and controllable facial expressions. Regarding existing procedures, limitations, and trends, this review suggests more efficient identification measures, the ethical design of AI, and better public education to combat the growing threat of deepfakes.