  The rapid advancement in deep learning makes the differentiation of authentic
and manipulated facial images and video clips unprecedentedly harder. The
underlying technology of manipulating facial appearances through deep
generative approaches, enunciated as DeepFake that have emerged recently by
promoting a vast number of malicious face manipulation applications.
Subsequently, the need of other sort of techniques that can assess the
integrity of digital visual content is indisputable to reduce the impact of the
creations of DeepFake. A large body of research that are performed on DeepFake
creation and detection create a scope of pushing each other beyond the current
status. This study presents challenges, research trends, and directions related
to DeepFake creation and detection techniques by reviewing the notable research
in the DeepFake domain to facilitate the development of more robust approaches
that could deal with the more advance DeepFake in the future.
