Security Benefits and Side Effects of Labeling AI-Generated Images
Journal:
arXiv
Published Date:
May 28, 2025
Abstract
Generative artificial intelligence is developing rapidly, impacting humans'
interaction with information and digital media. It is increasingly used to
create deceptively realistic misinformation, so lawmakers have imposed
regulations requiring the disclosure of AI-generated content. However, only
little is known about whether these labels reduce the risks of AI-generated
misinformation.
Our work addresses this research gap. Focusing on AI-generated images, we
study the implications of labels, including the possibility of mislabeling.
Assuming that simplicity, transparency, and trust are likely to impact the
successful adoption of such labels, we first qualitatively explore users'
opinions and expectations of AI labeling using five focus groups. Second, we
conduct a pre-registered online survey with over 1300 U.S. and EU participants
to quantitatively assess the effect of AI labels on users' ability to recognize
misinformation containing either human-made or AI-generated images. Our focus
groups illustrate that, while participants have concerns about the practical
implementation of labeling, they consider it helpful in identifying
AI-generated images and avoiding deception. However, considering security
benefits, our survey revealed an ambiguous picture, suggesting that users might
over-rely on labels. While inaccurate claims supported by labeled AI-generated
images were rated less credible than those with unlabeled AI-images, the belief
in accurate claims also decreased when accompanied by a labeled AI-generated
image. Moreover, we find the undesired side effect that human-made images
conveying inaccurate claims were perceived as more credible in the presence of
labels.
