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Framing digital inauthenticity: Comparing user detection of AI-generated faces to messaged-based scam methods
Dawn M Sarno1, John Solorio1, Shea Ballar1
1Clemson University, United States of America.
Acta Psychologica
|December 5, 2025
Summary
Users struggle to detect AI-generated faces compared to phishing scams. Individual differences like risk literacy and cognitive reflection influence detection of digital inauthenticity, highlighting the need for safeguards.
Area of Science:
- Cognitive Psychology
- Human-Computer Interaction
- Cybersecurity
Background:
- Generative AI (genAI) enables realistic impersonation via images and messages.
- Detecting message-based inauthenticity (e.g., phishing) is studied, but image-based detection is less understood.
- Cognitive mechanisms underlying detection of digital inauthenticity across modalities are unclear.
Purpose of the Study:
- Examine user similarity in detecting image- vs. message-based inauthenticity.
- Investigate if individual differences predict susceptibility to both types of inauthenticity.
- Identify other individual differences related to digital inauthenticity detection.
Main Methods:
- Participants classified real vs. phishing messages (email/text).
- Participants classified real vs. AI-generated faces.
- Assessed cognitive reasoning, digital/risk literacy, and demographics.
Main Results:
- Users are less adept at detecting image-based inauthenticity (AI faces) than message-based scams (phishing).
- Poorer, riskier classifications were made for AI-generated faces.
- Risk literacy, cognitive reflection, and gender correlated with detecting image-based inauthenticity.
Conclusions:
- Detection of digital inauthenticity varies by modality, with images posing greater challenges.
- Individual differences play a role in identifying AI-generated inauthenticity.
- Ongoing advancements in genAI necessitate improved technical safeguards and user interventions for digital security.
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