  Face image synthesis has progressed beyond the point at which humans can
effectively distinguish authentic faces from synthetically generated ones.
Recently developed synthetic face image detectors boast "better-than-human"
discriminative ability, especially those guided by human perceptual
intelligence during the model's training process. In this paper, we investigate
whether these human-guided synthetic face detectors can assist non-expert human
operators in the task of synthetic image detection when compared to models
trained without human-guidance. We conducted a large-scale experiment with more
than 1,560 subjects classifying whether an image shows an authentic or
synthetically-generated face, and annotate regions that supported their
decisions. In total, 56,015 annotations across 3,780 unique face images were
collected. All subjects first examined samples without any AI support, followed
by samples given (a) the AI's decision ("synthetic" or "authentic"), (b) class
activation maps illustrating where the model deems salient for its decision, or
(c) both the AI's decision and AI's saliency map. Synthetic faces were
generated with six modern Generative Adversarial Networks. Interesting
observations from this experiment include: (1) models trained with
human-guidance offer better support to human examination of face images when
compared to models trained traditionally using cross-entropy loss, (2) binary
decisions presented to humans offers better support than saliency maps, (3)
understanding the AI's accuracy helps humans to increase trust in a given model
and thus increase their overall accuracy. This work demonstrates that although
humans supported by machines achieve better-than-random accuracy of synthetic
face detection, the ways of supplying humans with AI support and of building
trust are key factors determining high effectiveness of the human-AI tandem.
