  The recent emergence of machine-manipulated media raises an important
societal question: how can we know if a video that we watch is real or fake? In
two online studies with 15,016 participants, we present authentic videos and
deepfakes and ask participants to identify which is which. We compare the
performance of ordinary human observers against the leading computer vision
deepfake detection model and find them similarly accurate while making
different kinds of mistakes. Together, participants with access to the model's
prediction are more accurate than either alone, but inaccurate model
predictions often decrease participants' accuracy. To probe the relative
strengths and weaknesses of humans and machines as detectors of deepfakes, we
examine human and machine performance across video-level features, and we
evaluate the impact of pre-registered randomized interventions on deepfake
detection. We find that manipulations designed to disrupt visual processing of
faces hinder human participants' performance while mostly not affecting the
model's performance, suggesting a role for specialized cognitive capacities in
explaining human deepfake detection performance.
