Data-Agnostic Face Image Synthesis Detection Using Bayesian CNNs
Roberto Leyva Victor Sanchez Gregory Epiphaniou Carsten Maple
Face image synthesis detection is considerably gaining attention because of
the potential negative impact on society that this type of synthetic data
brings. In this paper, we propose a data-agnostic solution to detect the face
image synthesis process. Specifically, our solution is based on an anomaly
detection framework that requires only real data to learn the inference
process. It is therefore data-agnostic in the sense that it requires no
synthetic face images. The solution uses the posterior probability with respect
to the reference data to determine if new samples are synthetic or not. Our
evaluation results using different synthesizers show that our solution is very
competitive against the state-of-the-art, which requires synthetic data for
training.