Abstract
This paper describes an exploratory study of deepfake video detection from a user’s perspective. Through semi-structured interviews, participants were asked to identify real and deepfake videos, and explain how they arrived at their conclusions. From the interviews, two sets of features were derived. One was associated with correct deepfake identification while the other was associated with incorrect identification. Interestingly, the two sets had overlapping features suggesting the difficulties associated with deepfake identification. Further, the majority of participants could not correctly identify all their assigned videos.
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Thaw, N.N., July, T., Wai, A.N., Goh, D.HL., Chua, A.Y.K. (2021). How Are Deepfake Videos Detected? An Initial User Study. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2021 - Posters. HCII 2021. Communications in Computer and Information Science, vol 1419. Springer, Cham. https://doi.org/10.1007/978-3-030-78635-9_80
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DOI: https://doi.org/10.1007/978-3-030-78635-9_80
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