  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 have 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 indepth 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
