Browse State-of-the-Art › Human Detection of Deepfakes
Human Detection of Deepfakes
1 paper with code · 0 benchmarks · 1 dataset archive 2025-07-28
The task of detecting deepfake stimuli, as given to human participants in a statistical study. Methodologies should ideally include a-priori power analysis (e.g. using the GPower software) to calculate the sample size of human participants that would be sufficient to detect the presence of a main effect of a specified effect size.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
1 dataset whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
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Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
1 shown of 1 paper with code (2 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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7 Dec 2022 2 repositories listedParticipants' confidence in their answers was high and unrelated to accuracy.
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