  The availability of large-scale facial databases, together with the
remarkable progresses of deep learning technologies, in particular Generative
Adversarial Networks (GANs), have led to the generation of extremely realistic
fake facial content, raising obvious concerns about the potential for misuse.
Such concerns have fostered the research on manipulation detection methods
that, contrary to humans, have already achieved astonishing results in various
scenarios. In this study, we focus on the synthesis of entire facial images,
which is a specific type of facial manipulation. The main contributions of this
study are four-fold: i) a novel strategy to remove GAN "fingerprints" from
synthetic fake images based on autoencoders is described, in order to spoof
facial manipulation detection systems while keeping the visual quality of the
resulting images; ii) an in-depth analysis of the recent literature in facial
manipulation detection; iii) a complete experimental assessment of this type of
facial manipulation, considering the state-of-the-art fake detection systems
(based on holistic deep networks, steganalysis, and local artifacts), remarking
how challenging is this task in unconstrained scenarios; and finally iv) we
announce a novel public database, named iFakeFaceDB, yielding from the
application of our proposed GAN-fingerprint Removal approach (GANprintR) to
already very realistic synthetic fake images.
  The results obtained in our empirical evaluation show that additional efforts
are required to develop robust facial manipulation detection systems against
unseen conditions and spoof techniques, such as the one proposed in this study.
