  The free access to large-scale public databases, together with the fast
progress of deep learning techniques, in particular Generative Adversarial
Networks, have led to the generation of very realistic fake content with its
corresponding implications towards society in this era of fake news. This
survey provides a thorough review of techniques for manipulating face images
including DeepFake methods, and methods to detect such manipulations. In
particular, four types of facial manipulation are reviewed: i) entire face
synthesis, ii) identity swap (DeepFakes), iii) attribute manipulation, and iv)
expression swap. For each manipulation group, we provide details regarding
manipulation techniques, existing public databases, and key benchmarks for
technology evaluation of fake detection methods, including a summary of results
from those evaluations. Among all the aspects discussed in the survey, we pay
special attention to the latest generation of DeepFakes, highlighting its
improvements and challenges for fake detection.
  In addition to the survey information, we also discuss open issues and future
trends that should be considered to advance in the field.
