  Fake face detection is a significant challenge for intelligent systems as
generative models become more powerful every single day. As the quality of fake
faces increases, the trained models become more and more inefficient to detect
the novel fake faces, since the corresponding training data is considered
outdated. In this case, robust One-Shot learning methods is more compatible
with the requirements of changeable training data. In this paper, we propose a
universal One-Shot GAN generated fake face detection method which can be used
in significantly different areas of anomaly detection. The proposed method is
based on extracting out-of-context objects from faces via scene understanding
models. To do so, we use state of the art scene understanding and object
detection methods as a pre-processing tool to detect the weird objects in the
face. Second, we create a bag of words given all the detected out-of-context
objects per all training data. This way, we transform each image into a sparse
vector where each feature represents the confidence score related to each
detected object in the image. Our experiments show that, we can discriminate
fake faces from real ones in terms of out-of-context features. It means that,
different sets of objects are detected in fake faces comparing to real ones
when we analyze them with scene understanding and object detection models. We
prove that, the proposed method can outperform previous methods based on our
experiments on Style-GAN generated fake faces.
