Deepfake Creation & Detection

Deep fake technology has emerged as a potent instrument for creating remarkably authentic synthetic content, encompassing images, videos, and audio recordings. While deep fakes offer promising applications in entertainment and content creation, they also give rise to notable concerns regarding misinformation, privacy violations, and societal manipulation. This paper provides a comprehensive review of deep fake creation techniques, including generative adversarial networks (GANs), autoencoders, and reinforcement learning-based approaches. Furthermore, it examines state-of-the- art methods for detecting and mitigating deep fakes, encompassing both traditional forensic techniques and cutting-edge deep learning algorithms. By synthesizing recent advancements in both deep fake generation and detection, this paper aims to contribute to a deeper understanding of the challenges and opportunities associated with this rapidly evolving technology.