Exploring Human Perception of AI-Generated Artworks

As image generators based on diffusion models and Generative Adversarial Networks (GANs) become widely accessible and user-friendly, it is crucial to explore the impact of AI-generated artworks. These creations, which rival traditional works, spark debates about authenticity and legal recognition. Advocates highlight creativity, while skeptics question human authorship. The boundary between AI and traditional art remains contentious, affecting issues of attribution and ownership. In our pilot study, individuals were asked (n = 45) to choose between handmade artworks, co-designed pieces, and text-prompt-generated images. Additionally, participants were inquired whether they could identify AI-generated images (n=30). Our findings reveal that while individuals can often distinguish AI-generated art (accuracy=70.37%), this does not impact their preference.

Authors

Institutions

Publication Details

Published
2024-09-18
DOI
https://doi.org/10.1109/istas61960.2024.10732054
Citations
3
Primary Topic
Aesthetic Perception and Analysis
Type
article
Field-Weighted Citation Impact
1.03
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring Human Perception of AI-Generated Artworks

Alejandro Lopez‐Rincon, David Rojas-Velázquez, Y. Dianey Rueda-Arango, Aleksandra V. Gorelova
3 citations
Aesthetic Perception and Analysis
1.03
article

Exploring Human Perception of AI-Generated Artworks

Alejandro Lopez‐Rincon, David Rojas-Velázquez, Y. Dianey Rueda-Arango, Aleksandra V. Gorelova
article en
3 citations

Abstract

As image generators based on diffusion models and Generative Adversarial Networks (GANs) become widely accessible and user-friendly, it is crucial to explore the impact of AI-generated artworks. These creations, which rival traditional works, spark debates about authenticity and legal recognition. Advocates highlight creativity, while skeptics question human authorship. The boundary between AI and traditional art remains contentious, affecting issues of attribution and ownership. In our pilot study, individuals were asked (n = 45) to choose between handmade artworks, co-designed pieces, and text-prompt-generated images. Additionally, participants were inquired whether they could identify AI-generated images (n=30). Our findings reveal that while individuals can often distinguish AI-generated art (accuracy=70.37%), this does not impact their preference.

Utrecht University (NL)
Openalex Percentile: Top 24%
Aesthetic Perception and Analysis
1.03
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Exploring Human Perception of AI-Generated Artworks — Alejandro Lopez‐Rincon, David Rojas-Velázquez, et al. · (2024) | TGRS Research Map | TGRS