"That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation
Sandra Höltervennhoff, Jonas Ricker, Maike M. Raphael, Charlotte Schwedes, Rebecca Weil, Asja Fischer, Thorsten Holz, Lea Schönherr, Sascha Fahl
Abstract
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 1 354 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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext b404b7e7-d718-4fac-a9c3-82e949cf1361Cited by top-tier papers1
Ask how each one uses itBuilds on24
- The Stable Signature: Rooting Watermarks in Latent Diffusion ModelsPierre Fernandez, Guillaume Couairon, Hervé Jégou, Matthijs Douze et al.ICCV 2023 · 370 citations
- Tree-Rings Watermarks: Invisible Fingerprints for Diffusion ImagesYuxin Wen, John Kirchenbauer, Jonas Geiping, Tom GoldsteinNeurIPS 2023 · 253 citations
- Effects of Credibility Indicators on Social Media News Sharing IntentWaheeb Yaqub, Otari Kakhidze, Morgan L. Brockman, Nasir D. Memon et al.CHI 2020 · 170 citations
- DRCT: Diffusion Reconstruction Contrastive Training towards Universal Detection of Diffusion Generated ImagesBaoying Chen, Jishen Zeng, Jianquan Yang, Rui YangICML 2024 · 124 citations
- Robustness of AI-Image Detectors: Fundamental Limits and Practical AttacksMehrdad Saberi, Vinu Sankar Sadasivan, Keivan Rezaei, Aounon Kumar et al.ICLR 2024 · 92 citations
Related papers
- Labeling Synthetic Content: User Perceptions of Label Designs for AI-Generated Content on Social MediaDilrukshi Gamage, Dilki Sewwandi, Min Zhang, Arosha K. BandaraCHI 2025 · 25 citations
- The Effects of AI-based Credibility Indicators on the Detection and Spread of Misinformation under Social InfluenceZhuoran Lu, Patrick Li, Weilong Wang, Ming YinCSCW 2022 · 55 citations
- A Representative Study on Human Detection of Artificially Generated Media Across CountriesJoel Frank, Franziska Herbert, Jonas Ricker, Lea Schönherr et al.S&P 2024 · 43 citations
- Double Face: Leveraging User Intelligence to Characterize and Recognize AI-synthesized FacesMatthew Joslin, Xian Wang, Shuang HaoUSENIX Security 2024 · 4 citations
- Dialogues with AI Reduce Beliefs in Misinformation but Build No Lasting Discernment SkillsAnku Rani, Valdemar Danry, Paul Pu Liang, Andrew Lippman et al.CHI 2026 · 3 citations
