Abstract
Deepfakes are synthetically generated media that pose as actual video recordings, and are a potential source of fake news or disinformation. Consequently, the ability to detect them is imperative. Although research has been done in creating algorithms for automatic detection, there is little work conducted on how users identify deepfakes. Hence, the present paper fills this gap with a user study. Through semi-structured interviews, participants were asked to identify real and deepfake videos, and explain how they arrived at their conclusions. Seven verification strategies emerged, with the most popular being the use of subtle indictors in the videos suggesting the presence of imperfections. The use of one’s social circle to verify a video was the least used. Surprisingly, only half our participants could correctly identify all the videos they watched. Deepfake videos that seemed to portray believable content or were of high quality made participants think they were real.
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Goh, D.HL., Lee, C.S., Chen, Z., Kuah, X.W., Pang, Y.L. (2022). Understanding Users’ Deepfake Video Verification Strategies. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2022 – Late Breaking Posters. HCII 2022. Communications in Computer and Information Science, vol 1655. Springer, Cham. https://doi.org/10.1007/978-3-031-19682-9_4
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