{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T17:59:12Z","timestamp":1758477552971,"version":"3.40.3"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031726637"},{"type":"electronic","value":"9783031726644"}],"license":[{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T00:00:00Z","timestamp":1729900800000},"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\/978-3-031-72664-4_1","type":"book-chapter","created":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T17:02:04Z","timestamp":1729875724000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Rethinking Data Bias: Dataset Copyright Protection via\u00a0Embedding Class-Wise Hidden Bias"],"prefix":"10.1007","author":[{"given":"Jinhyeok","family":"Jang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Byungok","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaehong","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chan-Hyun","family":"Youn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,26]]},"reference":[{"key":"1_CR1","unstructured":"https:\/\/image-net.org\/challenges\/LSVRC\/announcement-June-2-2015 , June 2015"},{"key":"1_CR2","unstructured":"https:\/\/www.kaggle.com\/c\/petfinder-adoption-prediction\/discussion\/125436, January 2020"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Aghakhani, H., Meng, D., Wang, Y.X., Kruegel, C., Vigna, G.: Bullseye polytope: a scalable clean-label poisoning attack with improved transferability. In: EuroS &P, pp. 159\u2013178 (2021)","DOI":"10.1109\/EuroSP51992.2021.00021"},{"key":"1_CR4","unstructured":"Baluja, S.: Hiding images in plain sight: deep steganography. In: NeurIPS, pp. 2066\u20132076 (2017)"},{"key":"1_CR5","unstructured":"Chen, X., Liu, C., Li, B., Lu, K., Song, D.: Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526 (2017)"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"2","key":"1_CR7","first-page":"303","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. ICCV 88(2), 303\u2013338 (2010)","journal-title":"ICCV"},{"key":"1_CR8","unstructured":"Geiping, J., et al.: Witches\u2019 brew: industrial scale data poisoning via gradient matching. In: ICLR (2021)"},{"key":"1_CR9","unstructured":"Goodfellow, I.J., et\u00a0al.: Challenges in representation learning: a report on three machine learning contests. In: NeurIPS (2013)"},{"key":"1_CR10","doi-asserted-by":"publisher","first-page":"47230","DOI":"10.1109\/ACCESS.2019.2909068","volume":"7","author":"T Gu","year":"2019","unstructured":"Gu, T., Liu, K., Dolan-Gavitt, B., Garg, S.: BadNets: evaluating backdooring attacks on deep neural networks. IEEE Access 7, 47230\u201347244 (2019)","journal-title":"IEEE Access"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Heo, B., Yun, S., Han, D., Chun, S., Choe, J., Oh, S.J.: Rethinking spatial dimensions of vision transformers. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.01172"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"1_CR13","unstructured":"Huang, W.R., Geiping, J., Fowl, L., Taylor, G., Goldstein, T.: MetaPoison: practical general-purpose clean-label data poisoning. In: NeurIPS, vol. 33, pp. 12080\u201312091 (2020)"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Jiang, W., Li, H., Xu, G., Zhang, T.: Color backdoor: a robust poisoning attack in color space. In: CVPR, pp. 8133\u20138142 (2023)","DOI":"10.1109\/CVPR52729.2023.00786"},{"key":"1_CR15","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"issue":"7","key":"1_CR16","first-page":"7","volume":"231N","author":"Y Le","year":"2015","unstructured":"Le, Y., Yang, X.: Tiny ImageNet visual recognition challenge. CS 231N(7), 7 (2015)","journal-title":"CS"},{"issue":"11","key":"1_CR17","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"1_CR18","unstructured":"Lee, J., Kim, E., Lee, J., Lee, J., Choo, J.: Learning debiased representation via disentangled feature augmentation. In: NeurIPS, vol. 34, pp. 25123\u201325133 (2021)"},{"key":"1_CR19","unstructured":"Li, Y., Bai, Y., Jiang, Y., Yang, Y., Xia, S.T., Li, B.: Untargeted backdoor watermark: towards harmless and stealthy dataset copyright protection. In: NeurIPS (2022)"},{"key":"1_CR20","unstructured":"Li, Y., Zhang, Z., Bai, J., Wu, B., Jiang, Y., Xia, S.T.: Open-sourced dataset protection via backdoor watermarking. In: NeurIPS Workshops (2020)"},{"key":"1_CR21","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, Y., Wu, B., Li, L., He, R., Lyu, S.: Invisible backdoor attack with sample-specific triggers. In: ICCV, pp. 16463\u201316472 (2021)","DOI":"10.1109\/ICCV48922.2021.01615"},{"key":"1_CR22","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":"1_CR23","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1109\/TIFS.2022.3155921","volume":"17","author":"G Liu","year":"2022","unstructured":"Liu, G., Xu, T., Ma, X., Wang, C.: Your model trains on my data? Protecting intellectual property of training data via membership fingerprint authentication. IEEE Trans. Inf. Forensics Secur. 17, 1024\u20131037 (2022)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"1_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1007\/978-3-030-58607-2_11","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Ma, X., Bailey, J., Lu, F.: Reflection backdoor: a natural backdoor attack on deep neural networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12355, pp. 182\u2013199. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58607-2_11"},{"key":"1_CR25","unstructured":"Nam, J., Cha, H., Ahn, S., Lee, J., Shin, J.: Learning from failure: de-biasing classifier from biased classifier. In: NeurIPS, vol. 33, pp. 20673\u201320684 (2020)"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Ramaswamy, V.V., Kim, S.S., Russakovsky, O.: Fair attribute classification through latent space de-biasing. In: CVPR, pp. 9301\u20139310 (2021)","DOI":"10.1109\/CVPR46437.2021.00918"},{"key":"1_CR27","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should i trust you?\u201d Explaining the predictions of any classifier. In: SIGKDD, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"1_CR28","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR, pp. 10684\u201310695 (2020)"},{"key":"1_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"1_CR30","unstructured":"Sablayrolles, A., Douze, M., Schmid, C., J\u00e9gou, H.: Radioactive data: tracing through training. In: ICML, pp. 8326\u20138335 (2020)"},{"key":"1_CR31","doi-asserted-by":"crossref","unstructured":"Saha, A., Subramanya, A., Pirsiavash, H.: Hidden trigger backdoor attacks. In: AAAI, vol. 34, pp. 11957\u201311965 (2020)","DOI":"10.1609\/aaai.v34i07.6871"},{"key":"1_CR32","unstructured":"Schwarzschild, A., Goldblum, M., Gupta, A., Dickerson, J.P., Goldstein, T.: Just how toxic is data poisoning? A unified benchmark for backdoor and data poisoning attacks. In: ICML, pp. 9389\u20139398 (2021)"},{"key":"1_CR33","unstructured":"Shafahi, A., et al.: Poison frogs! Targeted clean-label poisoning attacks on neural networks. In: NeurIPS, vol. 31 (2018)"},{"key":"1_CR34","unstructured":"Souri, H., Fowl, L., Chellappa, R., Goldblum, M., Goldstein, T.: Sleeper agent: scalable hidden trigger backdoors for neural networks trained from scratch. In: NeurIPS, vol. 35, pp. 19165\u201319178 (2022)"},{"issue":"1","key":"1_CR35","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. JMLR 15(1), 1929\u20131958 (2014)","journal-title":"JMLR"},{"key":"1_CR36","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: ICML, pp. 6105\u20136114 (2019)"},{"key":"1_CR37","doi-asserted-by":"crossref","unstructured":"Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C.: Efficient object localization using convolutional networks. In: CVPR, pp. 648\u2013656 (2015)","DOI":"10.1109\/CVPR.2015.7298664"},{"key":"1_CR38","unstructured":"Touvron, H., et\u00a0al.: ResMLP: feedforward networks for image classification with data-efficient training. In: ICLR (2021)"},{"key":"1_CR39","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1007\/978-3-031-19778-9_23","volume-title":"ECCV 2022","author":"T Wang","year":"2022","unstructured":"Wang, T., Yao, Y., Xu, F., An, S., Tong, H., Wang, T.: An invisible black-box backdoor attack through frequency domain. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13673, pp. 396\u2013413. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19778-9_23"},{"key":"1_CR40","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: PVT v2: improved baselines with pyramid vision transformer. Comput. Vis. Media, pp. 1\u201310 (2022)","DOI":"10.1007\/s41095-022-0274-8"},{"issue":"4","key":"1_CR41","first-page":"600","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. TIP 13(4), 600\u2013612 (2004)","journal-title":"TIP"},{"key":"1_CR42","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)"},{"key":"1_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Model watermarking for image processing networks. In: AAAI, vol.\u00a034, pp. 12805\u201312812 (2020)","DOI":"10.1609\/aaai.v34i07.6976"},{"key":"1_CR44","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Xie, L., Yuille, A.: Object recognition with and without objects. In: IJCAI, pp. 3609\u20133615 (2017)","DOI":"10.24963\/ijcai.2017\/505"}],"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\/978-3-031-72664-4_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T17:02:19Z","timestamp":1729875739000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72664-4_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,26]]},"ISBN":["9783031726637","9783031726644"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72664-4_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,26]]},"assertion":[{"value":"26 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"}}]}}