# Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individuals

arXiv: https://arxiv.org/abs/2505.16057 · HTML: https://mlsanity.com/paper/2505.16057/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 122 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 25 of 25)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https://mlsanity.com/paper/1612.03242/) (2016)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models](https://mlsanity.com/paper/2302.04222/) (2023)
- [Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust](https://mlsanity.com/paper/2305.20030/) (2023)
- [Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances](https://mlsanity.com/paper/2410.18775/) (2024)
- [Neural Voice Cloning with a Few Samples](https://mlsanity.com/paper/1802.06006/) (2018)
- [An Undetectable Watermark for Generative Image Models](https://mlsanity.com/paper/2410.07369/) (2024)
- [Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds](https://mlsanity.com/paper/2105.06496/) (2021)
- [An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio](https://mlsanity.com/paper/2208.09646/) (2022)
- [Governance of Generative AI in Creative Work: Consent, Credit, Compensation, and Beyond](https://mlsanity.com/paper/2501.11457/) (2025)
- [Foregrounding Artist Opinions: A Survey Study on Transparency, Ownership, and Fairness in AI Generative Art](https://mlsanity.com/paper/2401.15497/) (2024)
- [Towards Understanding of Deepfake Videos in the Wild](https://mlsanity.com/paper/2309.01919/) (2023)
- [Examining the Impact of Provenance-Enabled Media on Trust and Accuracy Perceptions](https://mlsanity.com/paper/2303.12118/) (2023)
- [AMP: Authentication of Media via Provenance](https://mlsanity.com/paper/2001.07886/) (2020)
- [Unmasking Illusions: Understanding Human Perception of Audiovisual Deepfakes](https://mlsanity.com/paper/2405.04097/) (2024)
- [Aligning AI with Public Values: Deliberation and Decision-Making for Governing Multimodal LLMs in Political Video Analysis](https://mlsanity.com/paper/2410.01817/) (2024)
- [Encounters with Visual Misinformation and Labels Across Platforms: An Interview and Diary Study to Inform Ecosystem Approaches to Misinformation Interventions](https://mlsanity.com/paper/2011.12758/) (2020)
- [Lightweight Model Attribution and Detection of Synthetic Speech via Audio Residual Fingerprints](https://mlsanity.com/paper/2411.14013/) (2024)
- [This photograph has been altered: Testing the effectiveness of image forensic labeling on news image credibility](https://mlsanity.com/paper/2101.07951/) (2021)
- [Effects of Automated Misinformation Warning Labels on the Intents to Like, Comment and Share Posts](https://mlsanity.com/paper/2403.12916/) (2024)
- [Preliminary Evaluation of Interactive Search Engine Interface for Visually Impaired Users](https://mlsanity.com/paper/1808.09885/) (2018)
- [Iterative Design of An Accessible Crypto Wallet for Blind Users](https://mlsanity.com/paper/2306.06261/) (2023)
- [Personhood Credentials: Human-Centered Design Recommendation Balancing Security, Usability, and Trust](https://mlsanity.com/paper/2502.16375/) (2025)

## Built on by (most cited first, 2 of 2)

- ["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)
- [More Human or More AI? Visualizing Human-AI Collaboration Disclosures in Journalistic News Production](https://mlsanity.com/paper/2601.11072/) (2026)

## Abstract (arXiv)

AI-Generated (AIG) content has become increasingly widespread by recent advances in generative models and the easy-to-use tools that have significantly lowered the technical barriers for producing highly realistic audio, images, and videos through simple natural language prompts. In response, platforms are adopting provable provenance with platforms recommending AIG to be self-disclosed and signaled to users. However, these indicators may be often missed, especially when they rely solely on visual cues and make them ineffective to users with different sensory abilities. To address the gap, we conducted semi-structured interviews (N=28) with 15 sighted and 13 BLV participants to examine their interaction with AIG content through self-disclosed AI indicators. Our findings reveal diverse mental models and practices, highlighting different strengths and weaknesses of content-based (e.g., title, description) and menu-aided (e.g., AI labels) indicators. While sighted participants leveraged visual and audio cues, BLV participants primarily relied on audio and existing assistive tools, limiting their ability to identify AIG. Across both groups, they frequently overlooked menu-aided indicators deployed by platforms and rather interacted with content-based indicators such as title and comments. We uncovered usability challenges stemming from inconsistent indicator placement, unclear metadata, and cognitive overload. These issues were especially critical for BLV individuals due to the insufficient accessibility of interface elements. We provide practical recommendations and design implications for future AIG indicators across several dimensions.