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Related Experiment Video

Updated: Jul 1, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Training humans to detect AI-generated faces.

Amy Dawel1, Tanya George1, Eric Y Mah2

  • 1School of Medicine and Psychology, The Australian National University, Canberra, ACT 2600, Australia.

Proceedings of the National Academy of Sciences of the United States of America
|June 29, 2026
PubMed
Summary

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This summary is machine-generated.

Training people to recognize subtle global impressions of AI faces significantly improved deepfake detection accuracy. This new method offers a more robust defense against AI-generated content than artifact-based detection.

Area of Science:

  • Computer Vision
  • Cognitive Psychology
  • Information Security

Background:

  • Deepfake technology generates realistic AI faces, threatening information integrity.
  • Current deepfake detection methods (algorithms, human artifact training) have limitations.
  • AI and human faces elicit distinct perceptual impressions.

Purpose of the Study:

  • To develop and evaluate a novel deepfake detection method.
  • To train participants to identify AI faces based on global perceptual impressions.
  • To assess the effectiveness and scalability of this training approach.

Main Methods:

  • Participants were trained to focus on global facial impressions distinguishing AI from human faces.
  • A pre-post design with untrained test faces measured detection accuracy improvement.
Keywords:
artificial intelligencedeepfakesfacial impressionshuman trainingperson perception

Related Experiment Videos

Last Updated: Jul 1, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

  • Test-retest and online replication studies validated the findings.

    • Main Results:

    • Participant accuracy in detecting AI faces nearly doubled after training.
    • High performers achieved near-perfect detection rates.
    • Training enhanced participants' metacognitive insight and confidence calibration.

    Conclusions:

    • Training individuals to perceive global features offers a promising, durable deepfake detection strategy.
    • This approach addresses inherent biases in generative AI, unlike artifact-specific methods.
    • The method is scalable and effective, providing a new defense against AI-generated disinformation.