mlsanityData to 14 Oct 2026

Interoperable Provenance Authentication of Broadcast Media using Open Standards-based Metadata, Watermarking and Cryptography

arXiv 2405.12336 ↗First posted 20 May 2024cs.CR cs.MMcited by 6

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Abstract (arXiv)

The spread of false and misleading information is receiving significant attention from legislative and regulatory bodies. Consumers place trust in specific sources of information, so a scalable, interoperable method for determining the provenance and authenticity of information is needed. In this paper we analyze the posting of broadcast news content to a social media platform, the role of open standards, the interplay of cryptographic metadata and watermarks when validating provenance, and likely success and failure scenarios. We conclude that the open standards for cryptographically authenticated metadata developed by the Coalition for Provenance and Authenticity (C2PA) and for audio and video watermarking developed by the Advanced Television Systems Committee (ATSC) are well suited to address broadcast provenance. We suggest methods for using these standards for optimal success.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 May 2024.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 4.

6 papers.

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  1. 2026Media Integrity and Authentication: Status, Directions, and Futures
  2. 2026Watermarks and Fingerprints as Soft Bindings for Content Provenance: An Open-Licence Benchmark for Images, Audio and Video
  3. 2024RealSeal: Revolutionizing Media Authentication with Real-Time Realism Scoring
  4. 2026Tracing Provenance and Detecting Tampering with Complementary LLM Watermarks
  5. 2026TimeMark: A Trustworthy Time Watermarking Framework for Exact Generation-Time Recovery from AIGC
  6. 2026LLM Watermarking as Big Data Provenance: A Deployment-Oriented Systematization
  7. 2026Soft Redaction of Image Provenance via Zero-Knowledge Proofs
  8. 2025Auditing Data Provenance in Real-world Text-to-Image Diffusion Models for Privacy and Copyright Protection