BIAW-DL-SA: Blockchain-Integrated Audio Watermarking and Deep Learning for Ownership Verification and Forgery Detection in AI-Generated Music
Abstract
Copyright ownership and audio content recognition are pressing concerns as generative AI is increasingly used in music composition. AI-generated music authenticity and creator rights are now a major issue. Existing ownership verification methods lack robust, tamper-proof processes and often fail to detect deep learning model forgeries. Blockchain-Integrated Audio Watermarking with Deep Learning-Based Source Attribution (BIAW-DL-SA) embeds imperceptible watermarks using a convolutional autoencoder and registers metadata on a decentralized blockchain ledger for transparent, tamper-resistant verification. Fake or Real (FoR), Deepfake Voice Recognition (DFVR), Open Dataset Synthetic Speech (ODSS), Copy-Move Forgery Detection (CMFD), and CVoiceFake were used in evaluations. BIAW-DL-SA outperforms AIR-Fund, AICORE, and ROYAL-AI-M in attribution precision and ownership verification accuracy, reaching 95% with more registered tracks. BIAW-DL-SA maintains sub-120 ms latency even under severe request load, while competitor platforms experience latency growth. These findings prove BIAW-DL-SA is scalable, real-time, and tamper-resistant for music platforms, artists, and copyright organizations. The method improves AI-generated music copyright protection.References
Surbhi, A., & Roy, D. (2024, October). Tunes of tomorrow: Copyright and AI-generated music in the digital age. In AIP Conference Proceedings (Vol. 3220, No. 1, p. 050003). AIP Publishing LLC.
Bhargava, A. (2023). Copyright Law and Artificial Intelligence Generated Works: Ownership and Liability. Issue 2 Indian JL & Legal Rsch., 5, 1.
Oladele, O. K. (2024). Generative AI and Intellectual Property: Ownership, Copyright, and Creative Rights.
Jacques, S., & Flynn, M. (2024). Protecting human creativity in AI-generated music through effective licensing.
Gaffar, H., & Albarashdi, S. (2025). Copyright protection for AI-generated works: Exploring originality and ownership in a digital landscape. Asian Journal of International Law, 15(1), 23-46.
Fenwick, M., & Jurcys, P. (2023). Originality and the Future of Copyright in an Age of Generative AI. Computer Law & Security Review, 51, 105892.
Paquette, L. (2021). Artificial life imitating art imitating life: Copyright Ownership in AI-Generated Works. Intellectual Property Journal, 33(2), 183-215.
Aziz, A. (2023). Artificial intelligence produced original work: A new approach to copyright protection and ownership. European Journal of Artificial Intelligence and Machine Learning, 2(2), 9-16.
Smits, J., & Borghuis, T. (2022). Generative AI and intellectual property rights. In Law and artificial intelligence: Regulating AI and applying AI in legal practice (pp. 323-344). The Hague: TMC Asser Press.
Sun, H. (2021). Redesigning copyright protection in the era of artificial intelligence. Iowa L. Rev., 107, 1213.
Wang, J. T., Deng, Z., Chiba-Okabe, H., Barak, B., & Su, W. J. (2024). An economic solution to copyright challenges of generative ai. arXiv preprint arXiv:2404.13964.
Nordås, H. K. (2024). Copyright and trade in the digital music industry. In Handbook of Innovation and Intellectual Property Rights (pp. 177-190). Edward Elgar Publishing.
Alkato, A. A., & Sakhnin, Y. (2025). Advanced real-time anomaly detection and predictive trend modelling in smart systems using deep belief network architectures. PatternIQ Mining, 2(1), 97–107. https://doi.org/10.70023/sahd/250209
Shroff, L. (2024). AI & copyright: A case study of the music industry. GRACE: Global Review of AI Community Ethics, 2(1).
Bulayenko, O., Quintais, J. P., Gervais, D. J., & Poort, J. (2022). Ai music outputs: Challenges to the copyright legal framework. Available at SSRN 4072806.
Adebiyi, O. I., & Adeusi, O. C. (2025). Examining legal and ethical frameworks for protecting intellectual property rights in AI-generated content across creative industries.
Vivaldi, W., & Sutedja, I. (2024). Using deep learning and CBIR to address copyright concerns of AI-generated art: A systematic literature review. Devotion: Journal of Research and Community Service, 5(10), 1320-1330.
Jacques, S., & Flynn, M. (2024). Protecting human creativity in AI-generated music with the introduction of an AI-royalty fund. GRUR International, 73(12), 1137-1149.
Drott, E. (2021). Copyright, Compensation, and Commons in the Music AI Industry. Creative Industries Journal, 14(2), 190-207.
Ren, J., Xu, H., He, P., Cui, Y., Zeng, S., Zhang, J., ... & Tang, J. (2024). Copyright protection in generative AI: A technical perspective. arXiv preprint arXiv:2402.02333.
Dzuong, J., Wang, Z., & Zhang, W. (2024). Uncertain boundaries: Multidisciplinary approaches to copyright issues in generative ai. arXiv preprint arXiv:2404.08221.
Bukhari, S. W. R., Hassan, S. U., & Aleem, Y. (2023). Impact of artificial intelligence on copyright law: Challenges and prospects. Journal of the Law Society of Scotland, 5(4), 647-656.
Pujari, V., & Wilson, B. (2023). Copyright and authorship in AI-Generated music. Journal of Emerging Technologies and Innovative Research, 10(12), 351-354.
Deng, J., Zhang, S., & Ma, J. (2023). Computational copyright: Towards a royalty model for music generative ai. arXiv preprint arXiv:2312.06646.
Deng, J., & Ma, J. (2023). Computational copyright: Towards a royalty model for ai music generation platforms. In ICLR 2024 Workshop on Navigating and Addressing Data Problems for Foundation Models.
https://www.kaggle.com/datasets/birdy654/deep-voice-deepfake-voice-recognition/data
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