  DeepFake involves the use of deep learning and artificial intelligence
techniques to produce or change video and image contents typically generated by
GANs. Moreover, it can be misused and leads to fictitious news, ethical and
financial crimes, and also affects the performance of facial recognition
systems. Thus, detection of real or fake images is significant specially to
authenticate originality of people's images or videos. One of the most
important challenges in this topic is obstruction that decreases the system
precision. In this study, we present a deep learning approach using the entire
face and face patches to distinguish real/fake images in the presence of
obstruction with a three-path decision: first entire-face reasoning, second a
decision based on the concatenation of feature vectors of face patches, and
third a majority vote decision based on these features. To test our approach,
new datasets including real and fake images are created. For producing fake
images, StyleGAN and StyleGAN2 are trained by FFHQ images and also StarGAN and
PGGAN are trained by CelebA images. The CelebA and FFHQ datasets are used as
real images. The proposed approach reaches higher results in early epochs than
other methods and increases the SoTA results by 0.4\%-7.9\% in the different
built data-sets. Also, we have shown in experimental results that weighing the
patches may improve accuracy.
