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Cited by 2Law, AI, and Intellectual PropertyIntellectual Property Rights and MediaDigitalization, Law, and Regulation

Developing Standardized Metadata Protocols Enabling Transparent Provenance Tracking for AI-Created Media Within Federal Intellectual Property Regulatory Systems Nationwide

Open access
·2025·International Journal of Computer Applications Technology and Research
DOI 10.7753/ijcatr1112.1031
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Abstract

The rapid proliferation of artificial intelligence (AI) systems capable of generating images, audio, video, and written content has challenged the foundations of traditional intellectual property (IP) governance and evidence frameworks across federal regulatory systems.As AI-generated media becomes increasingly indistinguishable from human-authored works, questions of authorship, ownership, authenticity, and liability have become significantly more complex.Current federal IP regulations, originally built around clear human creative intent and manual documentation, lack standardized mechanisms for verifying the provenance and creative lineage of AI-derived outputs.This gap not only complicates copyright claims and patent defenses but also opens pathways for misinformation campaigns, fraudulent media manipulation, and digital marketplace disputes.To address these concerns, the development of standardized metadata protocols is emerging as a critical priority.Such protocols would embed persistent, machinereadable provenance markers capturing model origin, training data sources, transformation history, and authorship contributions into AI-created media at the point of generation.When implemented across federal IP workflows, these metadata structures would enable transparent chain-of-custody tracking and facilitate reliable audit, authentication, and enforcement procedures.The proposed framework requires interoperability across commercial AI tools, regulatory databases, and digital asset registries, supported by secure hashing, digital signatures, and tamper-evident storage.By institutionalizing standardized metadata protocols nationwide, federal systems can better safeguard creative rights, uphold marketplace integrity, and promote responsible innovation, ensuring that AImediated creativity remains verifiable, accountable, and legally protected.

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  • Adversarial edits may target specific identifiers such as model version numbers or generative session logs, preventing traceability across distribution chains.

    “These edits may target specific identifiers such as model version numbers or generative session logs, preventing traceability across distribution chains [29].”

    From Designing automated audit mechanisms to evaluate compliance of generative AI platforms with federal authorship and ownership disclosure requirements. (2023) · cites this paper as [29]

    Section 4.3 (page unavailable) says actors may intentionally erase or modify provenance metadata, and that protections must persist through distribution. It does not say edits specifically target model version numbers or generative session logs; model versioning appears there as information that provenance logs can document, not as an identified target of edits. (LLM, conf=0.84)

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Research works printed but not linked: 31

  1. Jahankhani H, Kendzierskyj S, Montasari R, Chelvachandran N, editors. Social Media Analytics, Strategies and Governance. Taylor & Francis Group; 2022 Aug 18.
  2. Benhamou Y, Ferland J. Digitization of GLAM collections and copyright: Policy paper. GRUR International. 2022 May 1;71(5):403-21.
  3. Benhamou Y, Ferland J, Renold MA. Digitization of GLAM Collections: Policy Paper. Available at SSRN 3963359. 2021 Nov 14.
  4. Sharp AJ. Head in the Bitcloud: A Discussion on the Copyrightability and Ownership Rights in Generative Digital Art and Non-Fungible Tokens. San Diego L. Rev.. 2022;59:637.
  5. Emmanuel Damilola Atanda. EXAMINING HOW ILLIQUIDITY PREMIUM IN PRIVATE CREDIT COMPENSATES ABSENCE OF MARK-TO-MARKET OPPORTUNITIES UNDER NEUTRAL INTEREST RATE ENVIRONMENTS. International Journal Of Engineering Technology Research & Management (IJETRM). 2018Dec21;02(12):151-64.
  6. Bonadio E, Lucchi N. How Far Can Copyright Be Stretched?-Framing the Debate on Whether New and Different Forms of Creativity Can Be Protected. Intellectual Property Quarterly. 2019 Apr 1;2019(2):115- 35.
  7. Nixon L, Dasiopoulou S, Evain JP, Hyvönen E, Kompatsiaris I, Troncy R. Multimedia, broadcasting, and eculture. InHandbook of Semantic Web Technologies 2011 (pp. 911-975). Springer, Berlin, Heidelberg.
  8. Gheran BF, Villarreal-Narvaez S, Vatavu RD, Vanderdonckt J. RepliGES and GEStory: visual tools for systematizing and consolidating knowledge on user- defined gestures. InProceedings of the 2022 International Conference on Advanced Visual Interfaces 2022 Jun 6 (pp. 1-9).
  9. Rumbidzai Derera. HOW FORENSIC ACCOUNTING TECHNIQUES CAN DETECT EARNINGS MANIPULATION TO PREVENT MISPRICED CREDIT DEFAULT SWAPS AND BOND UNDERWRITING FAILURES. International Journal of Engineering Technology Research & Management (IJETRM). 2017Dec21;01(12):112-27.
  10. Bakos A, Miksa T, Rauber A. Research data preservation using process engines and machine-actionable data management plans. InInternational Conference on Theory and Practice of Digital Libraries 2018 Sep 5 (pp. 69-80). Cham: Springer International Publishing.
  11. Burny N, Vanderdonckt J. GUIMETRICS: An Extensible Cloud-based Application for Automatic Computation of GUI Visual Design Measures. InICSOFT 2021 (pp. 505- 512).
  12. Shrestha A, Mahmood A. Review of deep learning algorithms and architectures. IEEE access. 2019 Apr 22;7:53040-65.
  13. Daniel ONI. TOURISM INNOVATION IN THE U.S. THRIVES THROUGH GOVERNMENTBACKED HOSPITALITY PROGRAMS EMPHASIZING CULTURAL PRESERVATION, ECONOMIC GROWTH, AND INCLUSIVITY. International Journal Of Engineering Technology Research & Management (IJETRM). 2022Dec21;06(12):132-45.
  14. Atanda ED. Dynamic risk-return interactions between crypto assets and traditional portfolios: testing regime- switching volatility models, contagion, and hedging effectiveness. International Journal of Computer Applications Technology and Research. 2016;5(12):797- 807.
  15. Yu F, Xiu X, Li Y. A survey on deep transfer learning and beyond. Mathematics. 2022 Oct 3;10(19):3619.
  16. Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A, Agarwal S. Language models are few-shot learners. Advances in neural information processing systems. 2020;33:1877-901.
  17. Jadon S, Jadon A. An overview of deep learning architectures in few-shot learning domain. arXiv preprint arXiv:2008.06365. 2020 Aug 12. · arXiv
  18. Zhu X, Gu Y, Xiao Z. Herbkg: Constructing a herbal- molecular medicine knowledge graph using a two-stage framework based on deep transfer learning. Frontiers in Genetics. 2022 Apr 27;13:799349.
  19. Li J, Sun A, Han J, Li C. A survey on deep learning for named entity recognition. IEEE transactions on knowledge and data engineering. 2020 Mar 17;34(1):50- 70.
  20. De Lange M, Aljundi R, Masana M, Parisot S, Jia X, Leonardis A, Slabaugh G, Tuytelaars T. A continual learning survey: Defying forgetting in classification tasks. IEEE transactions on pattern analysis and machine intelligence. 2021 Feb 5;44(7):3366-85.
  21. Izacard G, Lewis P, Lomeli M, Hosseini L, Petroni F, Schick T, Dwivedi-Yu J, Joulin A, Riedel S, Grave E. Few-shot learning with retrieval augmented language models. arXiv preprint arXiv:2208.03299. 2022 Aug;1(2):4. · arXiv
  22. Siebers P, Janiesch C, Zschech P. A survey of text representation methods and their genealogy. IEEE Access. 2022 Sep 12;10:96492-513.
  23. Barlaug N, Gulla JA. Neural networks for entity matching: A survey. ACM Transactions on Knowledge Discovery from Data (TKDD). 2021 Apr 21;15(3):1-37.
  24. Khatun A, Rahman A, Islam MS, Chowdhury HA, Tasnim A. Authorship attribution in bangla literature (aabl) via transfer learning using ulmfit. Transactions on Asian and Low-Resource Language Information Processing. 2020.
  25. Wörmann J, Bogdoll D, Brunner C, Bührle E, Chen H, Chuo EF, Cvejoski K, van Elst L, Gottschall P, Griesche S, Hellert C. Knowledge augmented machine learning with applications in autonomous driving: A survey. arXiv preprint arXiv:2205.04712. 2022 May 10. · arXiv
  26. Adadi A. A survey on data-efficient algorithms in big data era. Journal of Big Data. 2021 Jan 26;8(1):24.
  27. Ching T, Himmelstein DS, Beaulieu-Jones BK, Kalinin AA, Do BT, Way GP, Ferrero E, Agapow PM, Zietz M, Hoffman MM, Xie W. Opportunities and obstacles for deep learning in biology and medicine. Journal of the royal society interface. 2018 Apr 30;15(141):20170387.
  28. Wankhade M, Rao AC, Kulkarni C. A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review. 2022 Oct;55(7):5731-80.
  29. Joshi M, Pal A, Sankarasubbu M. Federated learning for healthcare domain-pipeline, applications and challenges. ACM Transactions on Computing for Healthcare. 2022 Nov 3;3(4):1-36.
  30. Ye E, Bai X, O'Hare N, Asgarieh E, Thadani K, Perez- Sorrosal F, Adiga S. Multilingual taxonomic web page classification for contextual targeting at yahoo. InProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2022 Aug 14 (pp. 4372-4380).
  31. Volk MJ, Lourentzou I, Mishra S, Vo LT, Zhai C, Zhao H. Biosystems design by machine learning. ACS synthetic biology. 2020 Jun 2;9(7):1514-33.

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Venue
International Journal of Computer Applications Technology and Research
Publisher
Association of Technology and Science
Published
2025-10-31
ISSN
2319-8656 (ISSN-L)
DOI
10.7753/ijcatr1112.1031
OpenAlex
W4415729390
Cited by
1
References
0
Citations by year
2026: 1
FWCI
0.40
Type
journal article
Language
en
Open access
Locations
International Journal of Computer Applications Technology and Research (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Law, AI, and Intellectual Property
  • Social Sciences › Political Science and International Relations › Intellectual Property Rights and Media
  • Social Sciences › Law › Digitalization, Law, and Regulation
Keywords
AI-generated content, provenance tracking, intellectual property regulation, copyright, authorship attribution, AI ownership, authenticity verification, digital signatures, interoperability, misinformation

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