  The tremendous success of deep learning for imaging applications has resulted
in numerous beneficial advances. Unfortunately, this success has also been a
catalyst for malicious uses such as photo-realistic face swapping of parties
without consent. Transferring one person's face from a source image to a target
image of another person, while keeping the image photo-realistic overall has
become increasingly easy and automatic, even for individuals without much
knowledge of image processing. In this study, we use deep transfer learning for
face swapping detection, showing true positive rates >96% with very few false
alarms. Distinguished from existing methods that only provide detection
accuracy, we also provide uncertainty for each prediction, which is critical
for trust in the deployment of such detection systems. Moreover, we provide a
comparison to human subjects. To capture human recognition performance, we
build a website to collect pairwise comparisons of images from human subjects.
Based on these comparisons, images are ranked from most real to most fake. We
compare this ranking to the outputs from our automatic model, showing good, but
imperfect, correspondence with linear correlations >0.75. Overall, the results
show the effectiveness of our method. As part of this study, we create a novel,
publicly available dataset that is, to the best of our knowledge, the largest
public swapped face dataset created using still images. Our goal of this study
is to inspire more research in the field of image forensics through the
creation of a public dataset and initial analysis.
