Deepfake is a generative deep learning algorithm that creates or changes facial features in a very realistic way, making it hard to differentiate the real from the fake features. It can be used to make movies look better, as well as to spread false information by imitating famous people. In this paper, many different ways to make a Deepfake are explained, analyzed and separated categorically. Using Deepfake datasets, models are trained and tested for reliability through experiments. Deepfakes are a type of facial manipulation that allow people to change their entire faces, identities, attributes, and expressions. The trends in the available Deepfake datasets are also discussed, with a focus on how they’ve changed. Using Deep learning, a general Deepfake detection model is made. Moreover, the problems in making and detecting Deepfakes are also mentioned. As a result of this survey, it is expected that the development of new Deepfake-based imaging tools will speed up in the future. This survey gives in-depth review of methods for manipulating images of face, and various techniques to spot altered face images. Four types of facial manipulation are specifically discussed which are attribute manipulation, expression swap, entire face synthesis, and identity swap. Across every manipulation category, we yield information on manipulation techniques, significant benchmarks for technical evaluation of counterfeit detection techniques, available public databases, and a summary of the outcomes of all such analyses. From all of the topics in the survey, we focus on the most recent development of Deepfake, showing its advances and obstacles in detecting fake images.