"That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation
Not on the map. Why?
Abstract (arXiv)
As generative AI is increasingly contributing to the spread of deceptively realistic misinformation, lawmakers have introduced regulations requiring the disclosure of AI-generated content. However, it is unclear if labels reduce the risk of users falling for AI-generated misinformation. To address this research gap, we study the effect of labels on users' perception and the implications of mislabeling, focusing on AI-generated images. We first explored users' opinions and expectations of labels using five focus groups. Although participants were wary of practical implementations, they considered labeling helpful in identifying AI-generated images and avoiding deception. Second, we conducted a survey with 1354 participants to assess how labels affect users' ability to recognize misinformation. While labels reduced participants' belief in false claims supported by AI-generated images, we found evidence of overreliance, leading to unintended side effects: Participants were more susceptible to false claims accompanied by human-made images, and were more hesitant to believe true claims illustrated with labeled AI-generated images.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 149 references that are in the map. First posted on arXiv 28 May 2025.
Builds on
20 of 149 references.
References · 20
- 2019CNN-generated images are surprisingly easy to spot... for nowcited by 842
- 2023Towards Universal Fake Image Detectors that Generalize Across Generative Modelscited by 483
- 2018HiDDeN: Hiding Data With Deep Networkscited by 384
- 2019StegaStamp: Invisible Hyperlinks in Physical Photographscited by 256
- 2024Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Modelscited by 131
- 2024AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Errorcited by 123
- 2018'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisionscited by 91
- 2024RingID: Rethinking Tree-Ring Watermarking for Enhanced Multi-Key Identificationcited by 87
- 2023Robustness of AI-Image Detectors: Fundamental Limits and Practical Attackscited by 75
- 2023A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspectivecited by 33
- 2024Using AI Assistants in Software Development: A Qualitative Study on Security Practices and Concernscited by 27
- 2024AMMeBa: A Large-Scale Survey and Dataset of Media-Based Misinformation In-The-Wildcited by 26
8 more
- 2023A Representative Study on Human Detection of Artificially Generated Media Across Countriescited by 22
- 2024An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscapecited by 16
- 2021"Look! It's a Computer Program! It's an Algorithm! It's AI!": Does Terminology Affect Human Perceptions and Evaluations of Algorithmic Decision-Making Systems?cited by 13
- 2023Examining the Impact of Provenance-Enabled Media on Trust and Accuracy Perceptionscited by 9
- 2023The effect of source disclosure on evaluation of AI-generated messages: A two-part studycited by 8
- 2020Adapting Security Warnings to Counter Online Disinformationcited by 7
- 2024Adversarial Robustness of AI-Generated Image Detectors in the Real Worldcited by 6
- 2025Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individualscited by 2
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.