Deepfakes have emerged as one of the most significant developments in contemporary computational media, representing a sophisticated convergence of machine learning, computer vision, and audiovisual synthesis. Enabled primarily by deep neural networks such as generative adversarial networks (GANs) and transformer-based architectures, Deepfakes are realistic video fabrications through sound and image alteration and substitution that synthesises human likeness, speech, and behaviours. Deepfakes function simultaneously as creative tools, political instruments, security risks, and epistemic disruptors. They have generated widespread scholarly, regulatory, and public concern by contributing to the reshaping of visual communication and posing significant challenges to established norms of authenticity. This entry defines Deepfakes, outlines their technological foundations, synthesises insights from current research and assesses implications for media industries, journalism, documentary, disinformation, governance, and digital culture.