{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T17:55:23Z","timestamp":1773856523755,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.31449\/inf.v50i10.10501","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:58Z","timestamp":1773832378000},"source":"Crossref","is-referenced-by-count":0,"title":["BIAW-DL-SA: Blockchain-Integrated Audio Watermarking and Deep Learning for Ownership Verification and Forgery Detection in AI-Generated Music"],"prefix":"10.31449","volume":"50","author":[{"given":"Keke","family":"Pan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,3,18]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10501\/6619","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10501\/6619","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:59Z","timestamp":1773832379000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/10501"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":0,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,3,18]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i10.10501","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,18]]}}}