# Examining the Impact of Provenance-Enabled Media on Trust and Accuracy Perceptions

arXiv: https://arxiv.org/abs/2303.12118 · HTML: https://mlsanity.com/paper/2303.12118/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2023.

## Built on by (most cited first, 9 of 9)

- [Think Fast, Think Slow, Think Critical: Designing an Automated Propaganda Detection Tool](https://mlsanity.com/paper/2402.19135/) (2024)
- [Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media](https://mlsanity.com/paper/2503.05711/) (2025)
- [Atlas: A Framework for ML Lifecycle Provenance & Transparency](https://mlsanity.com/paper/2502.19567/) (2025)
- [The Generative AI Paradox: GenAI and the Erosion of Trust, the Corrosion of Information Verification, and the Demise of Truth](https://mlsanity.com/paper/2601.00306/) (2026)
- ["That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation](https://mlsanity.com/paper/2505.22845/) (2025)
- [Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individuals](https://mlsanity.com/paper/2505.16057/) (2025)
- [PRNU-Bench: A Novel Benchmark and Model for PRNU-Based Camera Identification](https://mlsanity.com/paper/2509.17581/) (2025)
- [One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread](https://mlsanity.com/paper/2608.30311/) (2026)
- [Trustworthy synthetic visual media: Evidence across the media lifecycle](https://mlsanity.com/paper/2609.34232/) (2026)

## Abstract (arXiv)

In recent years, industry leaders and researchers have proposed to use technical provenance standards to address visual misinformation spread through digitally altered media. By adding immutable and secure provenance information such as authorship and edit date to media metadata, social media users could potentially better assess the validity of the media they encounter. However, it is unclear how end users would respond to provenance information, or how to best design provenance indicators to be understandable to laypeople. We conducted an online experiment with 595 participants from the US and UK to investigate how provenance information altered users' accuracy perceptions and trust in visual content shared on social media. We found that provenance information often lowered trust and caused users to doubt deceptive media, particularly when it revealed that the media was composited. We additionally tested conditions where the provenance information itself was shown to be incomplete or invalid, and found that these states have a significant impact on participants' accuracy perceptions and trust in media, leading them, in some cases, to disbelieve honest media. Our findings show that provenance, although enlightening, is still not a concept well-understood by users, who confuse media credibility with the orthogonal (albeit related) concept of provenance credibility. We discuss how design choices may contribute to provenance (mis)understanding, and conclude with implications for usable provenance systems, including clearer interfaces and user education.