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The free access to large-scale public databases, together with the fastprogress of deep learning techniques, in particular Generative AdversarialNetworks, have led to the generation of very realistic fake content with itscorresponding implications towards society in this era of fake news. Thissurvey provides a thorough review of techniques for manipulating face imagesincluding DeepFake methods, and methods to detect such manipulations. Inparticular, four types of facial manipulation are reviewed: i) entire facesynthesis, ii) identity swap (DeepFakes), iii) attribute manipulation, and iv)expression swap. For each manipulation group, we provide details regardingmanipulation techniques, existing public databases, and key benchmarks fortechnology evaluation of fake detection methods, including a summary of resultsfrom those evaluations. Among all the aspects discussed in the survey, we payspecial attention to the latest generation of DeepFakes, highlighting itsimprovements and challenges for fake detection.  In addition to the survey information, we also discuss open issues and futuretrends that should be considered to advance in the field.

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection