{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T16:12:50Z","timestamp":1776096770899,"version":"3.50.1"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031733895","type":"print"},{"value":"9783031733901","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73390-1_20","type":"book-chapter","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T16:24:01Z","timestamp":1730305441000},"page":"338-354","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["RingID: Rethinking Tree-Ring Watermarking for\u00a0Enhanced Multi-key Identification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7170-277X","authenticated-orcid":false,"given":"Hai","family":"Ci","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3948-6915","authenticated-orcid":false,"given":"Pei","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7028-3347","authenticated-orcid":false,"given":"Yiren","family":"Song","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7681-2166","authenticated-orcid":false,"given":"Mike Zheng","family":"Shou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"20_CR1","unstructured":"An, B., et\u00a0al.: Benchmarking the robustness of image watermarks. arXiv preprint arXiv:2401.08573 (2024)"},{"key":"20_CR2","unstructured":"Bansal, A., et al.: Certified neural network watermarks with randomized smoothing. In: International Conference on Machine Learning, pp. 1450\u20131465. PMLR (2022)"},{"issue":"3","key":"20_CR3","first-page":"8","volume":"2","author":"J Betker","year":"2023","unstructured":"Betker, J., et al.: Improving image generation with better captions. Comput. Sci. 2(3), 8 (2023)","journal-title":"Comput. Sci."},{"key":"20_CR4","unstructured":"Blattmann, A., et\u00a0al.: Stable video diffusion: scaling latent video diffusion models to large datasets. arXiv preprint arXiv:2311.15127 (2023)"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Cherti, M., et al.: Reproducible scaling laws for contrastive language-image learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132829 (2023)","DOI":"10.1109\/CVPR52729.2023.00276"},{"key":"20_CR6","unstructured":"Ci, H., Song, Y., Yang, P., Xie, J., Shou, M.Z.: WMAdapter: adding watermark control to latent diffusion models. arXiv preprint arXiv:2406.08337 (2024)"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Ci, H., Wang, C., Ma, X., Wang, Y.: Optimizing network structure for 3D human pose estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2262\u20132271 (2019)","DOI":"10.1109\/ICCV.2019.00235"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Ci, H., et al.: GFPose: learning 3D human pose prior with gradient fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4800\u20134810 (2023)","DOI":"10.1109\/CVPR52729.2023.00465"},{"key":"20_CR9","unstructured":"Cox, I.: Digital Watermarking and Steganography, vol. 2, pp. 893\u2013914. Morgan Kaufmann Google Schola (2007)"},{"key":"20_CR10","unstructured":"Cui, S., Guo, X., Zhang, Z.: Estimation and inference in ultrahigh dimensional partially linear single-index models. arXiv preprint arXiv:2404.04471 (2024)"},{"key":"20_CR11","unstructured":"Cui, S., Sudjianto, A., Zhang, A., Li, R.: Enhancing robustness of gradient-boosted decision trees through one-hot encoding and regularization. arXiv preprint arXiv:2304.13761 (2023)"},{"key":"20_CR12","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780\u20138794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Fernandez, P., Couairon, G., J\u00e9gou, H., Douze, M., Furon, T.: The stable signature: rooting watermarks in latent diffusion models. arXiv preprint arXiv:2303.15435 (2023)","DOI":"10.1109\/ICCV51070.2023.02053"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Fernandez, P., Sablayrolles, A., Furon, T., J\u00e9gou, H., Douze, M.: Watermarking images in self-supervised latent spaces. In: ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3054\u20133058. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9746058"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Guo, H., et al.: Practical deep dispersed watermarking with synchronization and fusion. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 7922\u20137932 (2023)","DOI":"10.1145\/3581783.3612015"},{"key":"20_CR16","unstructured":"Guo, Y., et al.: AnimateDiff: animate your personalized text-to-image diffusion models without specific tuning. arXiv preprint arXiv:2307.04725(2024)"},{"key":"20_CR17","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"20_CR18","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840\u20136851 (2020)"},{"key":"20_CR19","unstructured":"Kong, Z., Ping, W., Huang, J., Zhao, K., Catanzaro, B.: DiffWave: a versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761 (2020)"},{"issue":"5","key":"20_CR20","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1109\/83.918569","volume":"10","author":"CY Lin","year":"2001","unstructured":"Lin, C.Y., Wu, M., Bloom, J.A., Cox, I.J., Miller, M.L., Lui, Y.M.: Rotation, scale, and translation resilient watermarking for images. IEEE Trans. Image Process. 10(5), 767\u2013782 (2001)","journal-title":"IEEE Trans. Image Process."},{"key":"20_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Liu, R., Wu, R., Van\u00a0Hoorick, B., Tokmakov, P., Zakharov, S., Vondrick, C.: Zero-1-to-3: zero-shot one image to 3D object. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9298\u20139309 (2023)","DOI":"10.1109\/ICCV51070.2023.00853"},{"key":"20_CR23","unstructured":"Liu, Y., Li, Z., Backes, M., Shen, Y., Zhang, Y.: Watermarking diffusion model. arXiv preprint arXiv:2305.12502 (2023)"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Luo, X., Li, Y., Chang, H., Liu, C., Milanfar, P., Yang, F.: DVMark: a deep multiscale framework for video watermarking. IEEE Transactions on Image Processing (2023)","DOI":"10.1109\/TIP.2023.3251737"},{"key":"20_CR25","unstructured":"Midjourney.com: Midjourney. https:\/\/www.midjourney.com\/home"},{"key":"20_CR26","unstructured":"OpenAI: Dalle2. https:\/\/openai.com\/dall-e-2"},{"key":"20_CR27","unstructured":"Poole, B., Jain, A., Barron, J.T., Mildenhall, B.: DreamFusion: text-to-3D using 2D diffusion. arXiv preprint arXiv:2209.14988 (2022)"},{"key":"20_CR28","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"20_CR30","unstructured":"Singer, U., et\u00a0al.: Make-a-video: text-to-video generation without text-video data. arXiv preprint arXiv:2209.14792 (2022)"},{"key":"20_CR31","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"key":"20_CR32","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)"},{"key":"20_CR33","unstructured":"Song, Y., et.: ProcessPainter: learn painting process from sequence data. arXiv preprint arXiv:2406.06062 (2024)"},{"issue":"4","key":"20_CR34","doi-asserted-by":"publisher","first-page":"1410","DOI":"10.1109\/18.923724","volume":"47","author":"Y Steinberg","year":"2001","unstructured":"Steinberg, Y., Merhav, N.: Identification in the presence of side information with application to watermarking. IEEE Trans. Inf. Theory 47(4), 1410\u20131422 (2001)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"20_CR35","doi-asserted-by":"crossref","unstructured":"Tancik, M., Mildenhall, B., Ng, R.: StegaStamp: invisible hyperlinks in physical photographs. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132126 (2020)","DOI":"10.1109\/CVPR42600.2020.00219"},{"key":"20_CR36","unstructured":"Tevet, G., Raab, S., Gordon, B., Shafir, Y., Cohen-Or, D., Bermano, A.H.: Human motion diffusion model. arXiv preprint arXiv:2209.14916 (2022)"},{"key":"20_CR37","doi-asserted-by":"crossref","unstructured":"Uchida, Y., Nagai, Y., Sakazawa, S., Satoh, S.: Embedding watermarks into deep neural networks. In: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, pp. 269\u2013277 (2017)","DOI":"10.1145\/3078971.3078974"},{"key":"20_CR38","unstructured":"Wen, Y., Kirchenbauer, J., Geiping, J., Goldstein, T.: Tree-ring watermarks: fingerprints for diffusion images that are invisible and robust. arXiv preprint arXiv:2305.20030 (2023)"},{"key":"20_CR39","doi-asserted-by":"crossref","unstructured":"Wu, J.Z., et al.: Tune-a-video: one-shot tuning of image diffusion models for text-to-video generation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7623\u20137633 (2023)","DOI":"10.1109\/ICCV51070.2023.00701"},{"issue":"12","key":"20_CR40","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1364\/OE.3.000497","volume":"3","author":"XG Xia","year":"1998","unstructured":"Xia, X.G., Boncelet, C.G., Arce, G.R.: Wavelet transform based watermark for digital images. Opt. Express 3(12), 497\u2013511 (1998)","journal-title":"Opt. Express"},{"key":"20_CR41","doi-asserted-by":"crossref","unstructured":"Xiong, C., Qin, C., Feng, G., Zhang, X.: Flexible and secure watermarking for latent diffusion model. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 1668\u20131676 (2023)","DOI":"10.1145\/3581783.3612448"},{"key":"20_CR42","unstructured":"Yang, P., Ci, H., Song, Y., Shou, M.Z.: Steganalysis on digital watermarking: is your defense truly impervious? arXiv preprint arXiv:2406.09026 (2024)"},{"key":"20_CR43","doi-asserted-by":"crossref","unstructured":"Yu, N., Skripniuk, V., Abdelnabi, S., Fritz, M.: Artificial fingerprinting for generative models: rooting deepfake attribution in training data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 14448\u201314457 (2021)","DOI":"10.1109\/ICCV48922.2021.01418"},{"key":"20_CR44","doi-asserted-by":"crossref","unstructured":"Zhang, D.J., et al.: Show-1: marrying pixel and latent diffusion models for text-to-video generation. arXiv preprint arXiv:2309.15818 (2023)","DOI":"10.1007\/s11263-024-02271-9"},{"key":"20_CR45","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Protecting intellectual property of deep neural networks with watermarking. In: Proceedings of the 2018 on Asia Conference on Computer and Communications Security, pp. 159\u2013172 (2018)","DOI":"10.1145\/3196494.3196550"},{"key":"20_CR46","unstructured":"Zhang, K.A., Xu, L., Cuesta-Infante, A., Veeramachaneni, K.: Robust invisible video watermarking with attention. arXiv preprint arXiv:1909.01285 (2019)"},{"key":"20_CR47","unstructured":"Zhao, X., Zhang, K., Wang, Y.X., Li, L.: Generative autoencoders as watermark attackers: analyses of vulnerabilities and threats. arXiv preprint arXiv:2306.01953 (2023)"},{"key":"20_CR48","unstructured":"Zhao, Y., Pang, T., Du, C., Yang, X., Cheung, N.M., Lin, M.: A recipe for watermarking diffusion models. arXiv preprint arXiv:2303.10137 (2023)"},{"key":"20_CR49","doi-asserted-by":"crossref","unstructured":"Zhu, J., Kaplan, R., Johnson, J., Fei-Fei, L.: Hidden: hiding data with deep networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 657\u2013672 (2018)","DOI":"10.1007\/978-3-030-01267-0_40"},{"key":"20_CR50","doi-asserted-by":"publisher","first-page":"2430","DOI":"10.1109\/TPAMI.2023.3330935","volume":"46","author":"W Zhu","year":"2023","unstructured":"Zhu, W., et al.: Human motion generation: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 46, 2430\u20132449 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73390-1_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T16:34:48Z","timestamp":1730306088000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73390-1_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,31]]},"ISBN":["9783031733895","9783031733901"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73390-1_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,31]]},"assertion":[{"value":"31 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}