  Recently, image manipulation has achieved rapid growth due to the advancement
of sophisticated image editing tools. A recent surge of generated fake imagery
and videos using neural networks is DeepFake. DeepFake algorithms can create
fake images and videos that humans cannot distinguish from authentic ones.
(GANs) have been extensively used for creating realistic images without
accessing the original images. Therefore, it is become essential to detect fake
videos to avoid spreading false information. This paper presents a survey of
methods used to detect DeepFakes and datasets available for detecting DeepFakes
in the literature to date. We present extensive discussions and research trends
related to DeepFake technologies.
