# Content ARCs: Decentralized Content Rights in the Age of Generative AI

arXiv: https://arxiv.org/abs/2503.14519 · HTML: https://mlsanity.com/paper/2503.14519/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 14 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [TrustMark: Universal Watermarking for Arbitrary Resolution Images](https://mlsanity.com/paper/2311.18297/) (2023)
- [Video Seal: Open and Efficient Video Watermarking](https://mlsanity.com/paper/2412.09492/) (2024)
- [Evaluating Data Attribution for Text-to-Image Models](https://mlsanity.com/paper/2306.09345/) (2023)
- [An Economic Solution to Copyright Challenges of Generative AI](https://mlsanity.com/paper/2404.13964/) (2024)
- [ProMark: Proactive Diffusion Watermarking for Causal Attribution](https://mlsanity.com/paper/2403.09914/) (2024)
- [OSCAR-Net: Object-centric Scene Graph Attention for Image Attribution](https://mlsanity.com/paper/2108.03541/) (2021)
- [EKILA: Synthetic Media Provenance and Attribution for Generative Art](https://mlsanity.com/paper/2304.04639/) (2023)
- [DECORAIT -- DECentralized Opt-in/out Registry for AI Training](https://mlsanity.com/paper/2309.14400/) (2023)

## Built on by (most cited first, 3 of 3)

- [Learning Probabilistic Embeddings for Unsupervised Action Segmentation](https://mlsanity.com/paper/2607.05263/) (2026)
- [Soft Redaction of Image Provenance via Zero-Knowledge Proofs](https://mlsanity.com/paper/2608.07063/) (2026)
- [Signpost Watermarking: Joint Optimization for Visual Watermark Coexistence](https://mlsanity.com/paper/2608.10091/) (2026)

## Abstract (arXiv)

The rise of Generative AI (GenAI) has sparked significant debate over balancing the interests of creative rightsholders and AI developers. As GenAI models are trained on vast datasets that often include copyrighted material, questions around fair compensation and proper attribution have become increasingly urgent. To address these challenges, this paper proposes a framework called Content ARCs (Authenticity, Rights, Compensation). By combining open standards for provenance and dynamic licensing with data attribution, and decentralized technologies, Content ARCs create a mechanism for managing rights and compensating creators for using their work in AI training. We characterize several nascent works in the AI data licensing space within Content ARCs and identify where challenges remain to fully implement the end-to-end framework.