DETECTING AI-GENERATED DEEPFAKE IMAGES: STUDENTS PERCEPTIONS

The rapid advancement of generative artificial intelligence has enabled the creation of highly realistic synthetic images and videos, raising major concerns about information integrity, political manipulation, and public trust. This study investigates how effectively individuals can detect AI-generated deepfake images, focusing on human judgment as automated detection tools remain unreliable, biased, and easily bypassed. Using a mixed-methods design, political deepfake content was generated through multiple AI models and evaluated through a structured survey administered at Suleyman Demirel University. Results show that although participants frequently encounter AI-generated media, their confidence in distinguishing authentic from synthetic content does not consistently align with actual detection accuracy. Findings further reveal that deepfakes can appear highly credible and hold persuasive potential even when recognized as artificial. The study highlights the need for multi-layered responses, including improved detection technologies, stronger media literacy, and more robust regulatory frameworks to mitigate emerging societal risks.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-12-12
DOI
https://doi.org/10.5281/zenodo.17932598
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
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article

DETECTING AI-GENERATED DEEPFAKE IMAGES: STUDENTS PERCEPTIONS

Zenodo (CERN European Organization for Nuclear Research)
Misinformation and Its Impacts
article

DETECTING AI-GENERATED DEEPFAKE IMAGES: STUDENTS PERCEPTIONS

article en

Abstract

The rapid advancement of generative artificial intelligence has enabled the creation of highly realistic synthetic images and videos, raising major concerns about information integrity, political manipulation, and public trust. This study investigates how effectively individuals can detect AI-generated deepfake images, focusing on human judgment as automated detection tools remain unreliable, biased, and easily bypassed. Using a mixed-methods design, political deepfake content was generated through multiple AI models and evaluated through a structured survey administered at Suleyman Demirel University. Results show that although participants frequently encounter AI-generated media, their confidence in distinguishing authentic from synthetic content does not consistently align with actual detection accuracy. Findings further reveal that deepfakes can appear highly credible and hold persuasive potential even when recognized as artificial. The study highlights the need for multi-layered responses, including improved detection technologies, stronger media literacy, and more robust regulatory frameworks to mitigate emerging societal risks.

Zenodo (CERN European Organization for Nuclear Research)
Suleyman Demirel University (KZ)
Quality Education
Openalex Percentile: Top 26%
Misinformation and Its Impacts
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DETECTING AI-GENERATED DEEPFAKE IMAGES: STUDENTS PERCEPTIONS · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS