{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T01:20:56Z","timestamp":1787448056078,"version":"3.56.0"},"reference-count":0,"publisher":"River Publishers","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JWE"],"abstract":"<jats:p>As the World Wide Web evolves into the central infrastructure for AI-generated content (AIGC), ensuring the provenance of assets distributed via online platforms has become a critical challenge in Web Engineering. The uncontrolled propagation of Low-Rank Adaptation (LoRA) models facilitates unauthorized style mimicry, yet existing watermarks often fail to survive LoRA\u2019s parameter compression. To safeguard digital trust and creator rights, we propose an Adaptation-Agnostic Trace Verification method optimized for secure web ecosystems. Our approach combines deep learning-based watermarking with a Statistical Resonance Amplifier (SRA) to induce the transfer of high-frequency signals into model weights. Furthermore, to overcome the noise limitations of single-image analysis in distributed web applications, we introduce an ensemble-based detection technique. Experimental results validate the method\u2019s robustness, achieving an AUC-ROC of 0.891 even in highly restricted Rank 32 environments (using 100 generated images) and a near-perfect 0.999 at Rank 128, without degrading generation quality. This study presents a practical technology for AI governance and copyright protection, essential for ensuring the trustworthiness of AI-enhanced Web services.<\/jats:p>","DOI":"10.13052\/jwe1540-9589.2561","type":"journal-article","created":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:41:58Z","timestamp":1787445718000},"source":"Crossref","is-referenced-by-count":0,"title":["Statistical Signal Amplification for Watermark Verification in Low-Rank Diffusion Adaptations"],"prefix":"10.13052","author":[{"given":"Jinseok","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uijin","family":"Jang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongtae","family":"Shin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"5195","published-online":{"date-parts":[[2026,8,22]]},"container-title":["Journal of Web Engineering"],"original-title":[],"link":[{"URL":"https:\/\/journals.riverpublishers.com\/index.php\/JWE\/article\/download\/32483\/24153","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.riverpublishers.com\/index.php\/JWE\/article\/download\/32483\/24154","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.riverpublishers.com\/index.php\/JWE\/article\/download\/32483\/24153","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T00:41:58Z","timestamp":1787445718000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.riverpublishers.com\/index.php\/JWE\/article\/view\/32483"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,22]]},"references-count":0,"URL":"https:\/\/doi.org\/10.13052\/jwe1540-9589.2561","relation":{},"ISSN":["1544-5976","1540-9589"],"issn-type":[{"value":"1544-5976","type":"electronic"},{"value":"1540-9589","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8,22]]}}}