---
type: source
id: kobis-2021-fooled-twice
title: 'Fooled twice: People cannot detect deepfakes but think they can'
authors:
- Nils C. Köbis
- Barbora Doležalová
- Ivan Soraperra
year: 2021
venue: iScience
volume: 24(11)
pages: '103364'
doi: 10.1016/j.isci.2021.103364
peer_review: peer-reviewed-journal
evidence_tier: B
design: Pre-registered online behavioural experiment; real vs deepfake video classification; manipulations of financial incentives and awareness; confidence ratings
modality:
- video
stimuli: Face-swap deepfake videos and authentic videos
n_participants: 210
n_note: ''
topics:
- human-detection
- overconfidence
- response-bias
- incentives
summary: People could not reliably detect deepfake videos, were biased toward judging them authentic, overestimated their ability, and neither incentives nor awareness raising helped.
results:
- metric: Detection
  value: not reliable
  note: exact accuracy not yet checked
  source_location: abstract
- metric: Response bias
  value: deepfakes mistaken for authentic more than the reverse
  note: '''seeing-is-believing'' heuristic'
  source_location: abstract
- metric: Financial incentives
  value: no improvement
  note: ''
  source_location: abstract
- metric: Awareness raising
  value: no improvement
  note: ''
  source_location: abstract
- metric: Calibration
  value: overconfident
  note: ''
  source_location: abstract
moderators: []
limitations:
- Single generator family (2020-era face swaps)
verification:
  status: verified-abstract
  confidence: high
  checked: 2026-10-07
  via: Semantic Scholar API abstract
url: null
pmid: null
pmcid: null
arxiv_id: null
openalex_id: null
oa_url: null
cited_in:
- MR-0001
updated: '2026-10-07'
---

# Fooled twice: People cannot detect deepfakes but think they can

> **Source record** `kobis-2021-fooled-twice` · 2021 · iScience  
> **Evidence tier:** B (peer-reviewed-journal) · **Verification:** verified-abstract (compiler confidence: high)  
> **Bottom line:** People could not reliably detect deepfake videos, were biased toward judging them authentic, overestimated their ability, and neither incentives nor awareness raising helped.

## Citation {#citation}

Nils C. Köbis, Barbora Doležalová, Ivan Soraperra (2021). Fooled twice: People cannot detect deepfakes but think they can. *iScience*, 24(11), 103364. https://doi.org/10.1016/j.isci.2021.103364

Verification entry (identifiers, resolvers, claims with source locations): [/data/citations.json#kobis-2021-fooled-twice](/data/citations.json#kobis-2021-fooled-twice)

## Study design {#design}

| Field | Value |
|---|---|
| Design | Pre-registered online behavioural experiment; real vs deepfake video classification; manipulations of financial incentives and awareness; confidence ratings |
| Modality | video |
| Stimuli | Face-swap deepfake videos and authentic videos |
| Participants (N) | 210 |
| Topics | human-detection, overconfidence, response-bias, incentives |

## Results {#results}

| Metric | Value | Note |
|---|---|---|
| Detection | not reliable | exact accuracy not yet checked |
| Response bias | deepfakes mistaken for authentic more than the reverse | 'seeing-is-believing' heuristic |
| Financial incentives | no improvement |  |
| Awareness raising | no improvement |  |
| Calibration | overconfident |  |

## Limitations {#limitations}

- Single generator family (2020-era face swaps)

## Cited in {#cited-in}

- [MR-0001](/metareports/human-detection-accuracy.md): Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training

## Verification {#verification}

| Field | Value |
|---|---|
| Status | `verified-abstract` |
| Compiler confidence | high |
| Checked on | 2026-10-07 |
| Via | Semantic Scholar API abstract |

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