  Although vanilla Convolutional Neural Network (CNN) based detectors can
achieve satisfactory performance on fake face detection, we observe that the
detectors tend to seek forgeries on a limited region of face, which reveals
that the detectors is short of understanding of forgery. Therefore, we propose
an attention-based data augmentation framework to guide detector refine and
enlarge its attention. Specifically, our method tracks and occludes the Top-N
sensitive facial regions, encouraging the detector to mine deeper into the
regions ignored before for more representative forgery. Especially, our method
is simple-to-use and can be easily integrated with various CNN models.
Extensive experiments show that the detector trained with our method is capable
to separately point out the representative forgery of fake faces generated by
different manipulation techniques, and our method enables a vanilla CNN-based
detector to achieve state-of-the-art performance without structure
modification.
