  We propose a new method to detect deepfake images using the cue of the source
feature inconsistency within the forged images. It is based on the hypothesis
that images' distinct source features can be preserved and extracted after
going through state-of-the-art deepfake generation processes. We introduce a
novel representation learning approach, called pair-wise self-consistency
learning (PCL), for training ConvNets to extract these source features and
detect deepfake images. It is accompanied by a new image synthesis approach,
called inconsistency image generator (I2G), to provide richly annotated
training data for PCL. Experimental results on seven popular datasets show that
our models improve averaged AUC over the state of the art from 96.45% to 98.05%
in the in-dataset evaluation and from 86.03% to 92.18% in the cross-dataset
evaluation.
