Examining the Impact of Provenance-Enabled Media on Trust and Accuracy Perceptions
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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.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Mar 2023.
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Citing papers per year: 2024: 1; 2025: 5; 2026: 3.
9 papers.
- 2024Think Fast, Think Slow, Think Critical: Designing an Automated Propaganda Detection Toolcited by 10
- 2025Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Mediacited by 9
- 2025Atlas: A Framework for ML Lifecycle Provenance & Transparencycited by 8
- 2026The Generative AI Paradox: GenAI and the Erosion of Trust, the Corrosion of Information Verification, and the Demise of Truthcited by 5
- 2025"That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformationcited by 3
- 2025Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individualscited by 2
- 2025PRNU-Bench: A Novel Benchmark and Model for PRNU-Based Camera Identificationcited by 0
- 2026One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spreadcited by 0
- 2026Trustworthy synthetic visual media: Evidence across the media lifecyclecited by 0
- 2026Trustworthy synthetic visual media: Evidence across the media lifecycle28 Sep 2026
- 2026One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread31 Aug 2026
- 2026The Generative AI Paradox: GenAI and the Erosion of Trust, the Corrosion of Information Verification, and the Demise of Truth1 Jan 2026
- 2025PRNU-Bench: A Novel Benchmark and Model for PRNU-Based Camera Identification22 Sep 2025
- 2025"That's another doom I haven't thought about": A User Study on AI Labels as a Safeguard Against Image-Based Misinformation28 May 2025
- 2025Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individuals21 May 2025
- 2025Atlas: A Framework for ML Lifecycle Provenance & Transparency26 Feb 2025
- 2025Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media14 Feb 2025
- 2024Think Fast, Think Slow, Think Critical: Designing an Automated Propaganda Detection Tool29 Feb 2024