ArticleProceedings of the National Academy of Sciences of the United States of America2022
Deepfake detection by human crowds, machines, and machine-informed crowds.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- When combinations of humans and AI are useful: A systematic review and meta-analysis.Nature human behaviour · 2024Pooled it
- Human detection of AI-generated faces and voices is not domain-general.Scientific reports · 2026Article
- Is this real? Susceptibility to deepfakes in machines and humans.Cognitive research: principles and implications · 2026Article
- The continued influence of AI-generated deepfake videos despite transparency warnings.Communications psychology · 2026Article
- AI-generated images of familiar faces are indistinguishable from real photographs.Cognitive research: principles and implications · 2025Article
- Mitigating the harms of manipulated media: Confronting deepfakes and digital deception.PNAS nexus · 2025Article
- People are poorly equipped to detect AI-powered voice clones.Scientific reports · 2025Article
- Decoding deception: state-of-the-art approaches to deep fake detection.Frontiers in big data · 2025Article
- Voice clones sound realistic but not (yet) hyperrealistic.PloS one · 2025Article
- AI-enhanced collective intelligence.Patterns (New York, N.Y.) · 2024Review
- Human detection of political speech deepfakes across transcripts, audio, and video.Nature communications · 2024Article
- Cortical-striatal brain network distinguishes deepfake from real speaker identity.Communications biology · 2024Article
- Boosting wisdom of the crowd for medical image annotation using training performance and task features.Cognitive research: principles and implications · 2024Article
- Classification performance assessment for imbalanced multiclass data.Scientific reports · 2024Article
- Reply to: Artificial Intelligence Face Swapping: Promise and Peril in Health Care.Mayo Clinic proceedings. Digital health · 2024Article
- Deep learning-aided decision support for diagnosis of skin disease across skin tones.Nature medicine · 2024Article
- Human perception of art in the age of artificial intelligence.Frontiers in psychology · 2024Article
- Deepfake detection with and without content warnings.Royal Society open science · 2023Article
- Deepfakes and scientific knowledge dissemination.Scientific reports · 2023Article
- Deepfakes as a threat to a speaker and facial recognition: An overview of tools and attack vectors.Heliyon · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model's prediction are more accurate than either alone, but inaccurate model predictions often decrease participants' accuracy. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants' performance while mostly not affecting the model's performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.
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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.