[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86142-en":3,"doc-seo-86142-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},86142,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","MusicMark: A Robust Generative Watermarking Framework for Music Generation","AI music generation has accelerated alongside commercial platforms, creating demand for reliable watermarking that supports provenance and attribution. Existing audio watermarking methods often target speech and are difficult to transfer to music’s complex structure and acoustic richness. Many approaches are post-hoc, inserting imperceptible perturbations after generation, making them fragile under transformations and particularly vulnerable to neural codec re-synthesis. MusicMark embeds watermark messages into the semantic latent space during generation using a diffusion-model watermark adapter and joint training with robust attack augmentation, achieving strong robustness while preserving generation quality.","MusicMark: A Robust Generative Watermarking Framework for  \nMusic Generation  \nSeohwan Yun, Jeeyoung Yun, Yongjin Kim, Juyeon Lee, and Sungwoong Kim  \narXiv :2607 . 1 1 1 17v 1 [ cs . SD] 13 Jul 2026  \nAbstract—AI music generation has rapidly advanced alongside commercial platforms, raising the need for reliable watermarking for provenance and attribution. However, existing audio watermarking research has largely focused on speech, and applying speech-oriented methods to music is challenging due to music’s complex structure and rich acoustic texture. Most existing methods are post-hoc, adding imperceptible perturbations after generation rather than embedding watermarks as part of the content. This makes them fragile under various transformationsand especially vulnerable to neural codec re-synthesis, which can discard imperceptible residual signals. Moreover, since generation and watermarking are decoupled, the watermarking step can be bypassed or omitted, weakening provenance guarantees. To address these issues, we propose MusicMark, which, to the best of our knowledge, is the first generative watermarking framework for music. Specifically, MusicMark embeds watermark messages into the semantic latent space during generation, incorporating the watermark as part of the musical content and ensuring robustness against diverse attacks, particularly neural codec resynthesis. To this end, we introduce a watermark adapter into a diffusion-based generation model to embed watermark messages across denoising steps. The adapter and detector are trained with a joint objective that preserves fidelity by constraining watermarked latents close to their unwatermarked reference latents, while improving robustness through comprehensive attack augmentations. Experiments demonstrate that MusicMark substantially outperforms post-hoc baselines across diverse attacks including neural codec re-synthesis, while maintaining comparable generation quality. We further introduce a coversong attack, converting the singing voice while preserving musical content, and show that MusicMark remains more robust than post-hoc methods.  \nIndex Terms—Music Watermarking, Generative Watermarking, Music Generation, Provenance Verification, Cover Song.  \nI. INTRODUCTION  \nRECENT advances in open-source music generation mod  \nels [1]–[8] and commercial AI music platforms [9], [10] have expanded AI content generation beyond speech synthesis, allowing users to create and distribute high-quality music from text prompts, lyrics, and other high-level conditions. As such content rapidly proliferates, reliable music watermarking becomes increasingly important for verifying the provenance and attribution of generated music, including its origin and source model.  \nExisting audio watermarking research has largely focused on speech [11]–[18] . Applying these speech-oriented methods  \nThis work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.  \nSeohwan Yun, Jeeyoung Yun, Yongjin Kim and Sungwoong Kim are with the Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea.  \nJuyeon Lee is with the Department of Computer Engineering, Inha University, Incheon, Republic of Korea.  \nCorresponding author: Sungwoong Kim (e-mail: [swkim01@korea.ac.kr](swkim01@korea.ac.kr)).  \nto music is challenging, as music differs substantially from speech in signal characteristics, musical structure, and perceptual constraints. Music typically spans a broader frequency range, uses higher sampling rates, and contains polyphonic mixtures of instruments and vocals organized through melody, harmony, rhythm, and long-range temporal dependencies [19]–[21] . Moreover, since human listeners are highly sensitive to musical dissonance and small melodic or harmonic errors, even subtle watermark-induced perturbations can degrade perceived musical quality [19], [21] . Music representations","cbCaivm3aUdtoquU","https://ap.wps.com/l/cbCaivm3aUdtoquU","pdf",3026011,4,1,13,"English","en",105,"# Introduction\n## Motivation for reliable music provenance\n## Limitations of existing (speech-focused and post-hoc) watermarking\n## Generative watermarking and remaining challenges\n## Proposed approach: MusicMark and latent-space embedding","[{\"question\":\"Why are speech-oriented watermarking methods hard to apply to music?\",\"answer\":\"Music differs from speech in signal characteristics, musical structure, and perceptual constraints. It spans broader frequency ranges and includes polyphonic mixtures, making watermarking require preserving rich perceptual details and robustness under transformations.\"},{\"question\":\"What key weakness do post-hoc watermarking methods have?\",\"answer\":\"Post-hoc methods insert imperceptible perturbations after audio generation, so the watermark can be fragile to temporal changes, frequency-domain transforms, compression, and especially neural codec re-synthesis, which can discard residual signals not captured semantically.\"},{\"question\":\"How does MusicMark improve robustness compared with post-hoc baselines?\",\"answer\":\"MusicMark embeds watermark messages into the semantic latent space during generation. It introduces a watermark adapter into a diffusion-based model, trained jointly with a detector using objectives that keep watermarked latents close to unwatermarked references while strengthening robustness through comprehensive attack augmentations, including neural codec re-synthesis and a cover-song attack.\"}]",1784208882,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"musicmark-a-robust-generative-watermarking-framework-for-music-generation","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/musicmark-a-robust-generative-watermarking-framework-for-music-generation/86142/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-27","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are speech-oriented watermarking methods hard to apply to music?","Question",{"text":75,"@type":76},"Music differs from speech in signal characteristics, musical structure, and perceptual constraints. It spans broader frequency ranges and includes polyphonic mixtures, making watermarking require preserving rich perceptual details and robustness under transformations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What key weakness do post-hoc watermarking methods have?",{"text":80,"@type":76},"Post-hoc methods insert imperceptible perturbations after audio generation, so the watermark can be fragile to temporal changes, frequency-domain transforms, compression, and especially neural codec re-synthesis, which can discard residual signals not captured semantically.",{"name":82,"@type":73,"acceptedAnswer":83},"How does MusicMark improve robustness compared with post-hoc baselines?",{"text":84,"@type":76},"MusicMark embeds watermark messages into the semantic latent space during generation. It introduces a watermark adapter into a diffusion-based model, trained jointly with a detector using objectives that keep watermarked latents close to unwatermarked references while strengthening robustness through comprehensive attack augmentations, including neural codec re-synthesis and a cover-song attack.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]