A Representative Study on Human Detection of Artificially Generated Media Across Countries

Source record frank-2024-representative-study · 2024 · IEEE Symposium on Security and Privacy (S&P 2024)
Evidence tier: B (peer-reviewed-conference) · Verification: verified-abstract (compiler confidence: high)
Bottom line: Most participants were effectively guessing; AI-generated media was rated more human-like than real media across all media types and countries.

Citation

Joel Frank, Franziska Herbert, Jonas Ricker, Lea Schönherr, Thorsten Eisenhofer, Asja Fischer, Markus Dürmuth, Thorsten Holz (2024). A Representative Study on Human Detection of Artificially Generated Media Across Countries. IEEE Symposium on Security and Privacy (S&P 2024). https://arxiv.org/abs/2312.05976

Verification entry (identifiers, resolvers, claims with source locations): /data/citations.json#frank-2024-representative-study

Study design

Field	Value
Design	Online survey with representative samples in USA, Germany, China; audio, image and text; regression on individual predictors
Modality	image-face, audio, text
Stimuli	State-of-the-art (2023) generated audio, images and text vs human-made counterparts
Participants (N)	3002
Topics	human-detection, cross-cultural, hyperrealism

Results

Metric	Value	Note
Overall	near guessing	abstract gives no percentages
AI vs real 'human-like' ratings	AI rated more human-like	all media types, all three countries
Predictors	generalised trust, cognitive reflection, self-reported deepfake familiarity significantly influenced decisions	across all media

Limitations

No video
Fixed stimulus set

Cited in

MR-0001: Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

Verification

Field	Value
Status	`verified-abstract`
Compiler confidence	high
Checked on	2026-10-07
Via	arXiv 2312.05976 abstract; S&P 2024 venue and DOI not confirmed

Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.