This survey provides a comprehensive overview of the latest advancements in deepfake generation and detection. It begins by unifying task definitions, introducing datasets, metrics, and evaluating methods. The survey then delves into four main deepfake fields: face swapping, face reenactment, talking face generation, and facial attribute editing, as well as forgery detection. Each section discusses the technological evolution, representative methods, and performance benchmarks. The survey also covers related sub-fields such as head swapping, face super-resolution, face reconstruction, and body animation. Finally, it analyzes the challenges and future research directions in these areas, emphasizing the need for continuous improvement in both generation and detection technologies to address ethical concerns like privacy invasion and phishing attacks.This survey provides a comprehensive overview of the latest advancements in deepfake generation and detection. It begins by unifying task definitions, introducing datasets, metrics, and evaluating methods. The survey then delves into four main deepfake fields: face swapping, face reenactment, talking face generation, and facial attribute editing, as well as forgery detection. Each section discusses the technological evolution, representative methods, and performance benchmarks. The survey also covers related sub-fields such as head swapping, face super-resolution, face reconstruction, and body animation. Finally, it analyzes the challenges and future research directions in these areas, emphasizing the need for continuous improvement in both generation and detection technologies to address ethical concerns like privacy invasion and phishing attacks.