  GAN-based techniques that generate and synthesize realistic faces have caused
severe social concerns and security problems. Existing methods for detecting
GAN-generated faces can perform well on limited public datasets. However,
images from existing public datasets do not represent real-world scenarios well
enough in terms of view variations and data distributions (where real faces
largely outnumber synthetic faces). The state-of-the-art methods do not
generalize well in real-world problems and lack the interpretability of
detection results. Performance of existing GAN-face detection models degrades
significantly when facing imbalanced data distributions. To address these
shortcomings, we propose a robust, attentive, end-to-end network that can spot
GAN-generated faces by analyzing their eye inconsistencies. Specifically, our
model learns to identify inconsistent eye components by localizing and
comparing the iris artifacts between the two eyes automatically. Our deep
network addresses the imbalance learning issues by considering the AUC loss and
the traditional cross-entropy loss jointly. Comprehensive evaluations of the
FFHQ dataset in terms of both balanced and imbalanced scenarios demonstrate the
superiority of the proposed method.
