  The creation and the manipulation of facial appearance via deep generative
approaches, known as DeepFake, have achieved significant progress and promoted
a wide range of benign and malicious applications. The evil side of this new
technique poses another popular study, i.e., DeepFake detection aiming to
identify the fake faces from the real ones. With the rapid development of the
DeepFake-related studies in the community, both sides (i.e., DeepFake
generation and detection) have formed the relationship of the battleground,
pushing the improvements of each other and inspiring new directions, e.g., the
evasion of DeepFake detection. Nevertheless, the overview of such battleground
and the new direction is unclear and neglected by recent surveys due to the
rapid increase of related publications, limiting the in-depth understanding of
the tendency and future works.
  To fill this gap, in this paper, we provide a comprehensive overview and
detailed analysis of the research work on the topic of DeepFake generation,
DeepFake detection as well as evasion of DeepFake detection, with more than 191
research papers carefully surveyed. We present the taxonomy of various DeepFake
generation methods and the categorization of various DeepFake detection
methods, and more importantly, we showcase the battleground between the two
parties with detailed interactions between the adversaries (DeepFake
generation) and the defenders (DeepFake detection). The battleground allows
fresh perspective into the latest landscape of the DeepFake research and can
provide valuable analysis towards the research challenges and opportunities as
well as research trends and directions in the field of DeepFake generation and
detection. We also elaborately design interactive diagrams
(http://www.xujuefei.com/dfsurvey) to allow researchers to explore their own
interests on popular DeepFake generators or detectors.
