ArticleRoyal Society open science2023
Deepfake detection with and without content warnings.
Article in Royal Society open science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Artificial Intelligence in Scientific Writing: Balancing Innovation and Efficiency with Integrity: Perspectives and Position Statements of Global Andrology Forum Expert Group.The world journal of men's health · 2026Article
- Exploring the importance of shape on dynamic recognition of self-face or friend-face.Scientific reports · 2026Article
- Artificial Intelligence Physician Avatars for Patient Education: A Pilot Study.Journal of clinical medicine · 2025Article
- Labeling AI-generated media online.PNAS nexus · 2025Article
- Ethical Use of Artificial Intelligence for Scientific Writing: Current Trends.Journal of human lactation : official journal of International Lactation Consultant Association · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid advancement of 'deepfake' video technology-which uses deep learning artificial intelligence algorithms to create fake videos that look real-has given urgency to the question of how policymakers and technology companies should moderate inauthentic content. We conduct an experiment to measure people's alertness to and ability to detect a high-quality deepfake among a set of videos. First, we find that in a natural setting with no content warnings, individuals who are exposed to a deepfake video of neutral content are no more likely to detect anything out of the ordinary (32.9%) compared to a control group who viewed only authentic videos (34.1%). Second, we find that when individuals are given a warning that at least one video in a set of five is a deepfake, only 21.6% of respondents correctly identify the deepfake as the only inauthentic video, while the remainder erroneously select at least one genuine video as a deepfake.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.