  Recent advances in neural networks for content generation enable artificial
intelligence (AI) models to generate high-quality media manipulations. Here we
report on a randomized experiment designed to study the effect of exposure to
media manipulations on over 15,000 individuals' ability to discern
machine-manipulated media. We engineer a neural network to plausibly and
automatically remove objects from images, and we deploy this neural network
online with a randomized experiment where participants can guess which image
out of a pair of images has been manipulated. The system provides participants
feedback on the accuracy of each guess. In the experiment, we randomize the
order in which images are presented, allowing causal identification of the
learning curve surrounding participants' ability to detect fake content. We
find sizable and robust evidence that individuals learn to detect fake content
through exposure to manipulated media when provided iterative feedback on their
detection attempts. Over a succession of only ten images, participants increase
their rating accuracy by over ten percentage points. Our study provides initial
evidence that human ability to detect fake, machine-generated content may
increase alongside the prevalence of such media online.
