  The emergence of contemporary deepfakes has attracted significant attention
in machine learning research, as artificial intelligence (AI) generated
synthetic media increases the incidence of misinterpretation and is difficult
to distinguish from genuine content. Currently, machine learning techniques
have been extensively studied for automatically detecting deepfakes. However,
human perception has been less explored. Malicious deepfakes could ultimately
cause public and social problems. Can we humans correctly perceive the
authenticity of the content of the videos we watch? The answer is obviously
uncertain; therefore, this paper aims to evaluate the human ability to discern
deepfake videos through a subjective study. We present our findings by
comparing human observers to five state-ofthe-art audiovisual deepfake
detection models. To this end, we used gamification concepts to provide 110
participants (55 native English speakers and 55 non-native English speakers)
with a webbased platform where they could access a series of 40 videos (20 real
and 20 fake) to determine their authenticity. Each participant performed the
experiment twice with the same 40 videos in different random orders. The videos
are manually selected from the FakeAVCeleb dataset. We found that all AI models
performed better than humans when evaluated on the same 40 videos. The study
also reveals that while deception is not impossible, humans tend to
overestimate their detection capabilities. Our experimental results may help
benchmark human versus machine performance, advance forensics analysis, and
enable adaptive countermeasures.
