{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:35:40Z","timestamp":1743064540324,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":33,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819616237"},{"type":"electronic","value":"9789819616244"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-981-96-1624-4_6","type":"book-chapter","created":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T06:53:53Z","timestamp":1738652033000},"page":"71-82","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VoteGAN: Generalized Membership Inference Attack Against Generative Models by\u00a0Multiple Discriminators"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5274-8135","authenticated-orcid":false,"given":"Gyeongsup","family":"Lim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wonjun","family":"Oh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4823-4194","authenticated-orcid":false,"given":"Junbeom","family":"Hur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"key":"6_CR1","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214\u2013223. PMLR (2017)"},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Bauer, L.A., Bindschaedler, V.: Towards realistic membership inferences: the case of survey data. In: Annual Computer Security Applications Conference, pp. 116\u2013128 (2020)","DOI":"10.1145\/3427228.3427282"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., Tramer, F.: Membership inference attacks from first principles. In: 2022 IEEE Symposium on Security and Privacy (SP), pp. 1519\u20131519. IEEE Computer Society (2022)","DOI":"10.1109\/SP46214.2022.9833649"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Caruana, R., Lawrence, S., Giles, C.: Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. In: Advances in Neural Information Processing Systems, vol. 13 (2000)","DOI":"10.1109\/IJCNN.2000.857823"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Chen, D., Yu, N., Zhang, Y., Fritz, M.: Gan-leaks: a taxonomy of membership inference attacks against generative models. In: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, pp. 343\u2013362 (2020)","DOI":"10.1145\/3372297.3417238"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Chen, J., Wang, W.H., Gao, H., Shi, X.: Par-GAN: improving the generalization of generative adversarial networks against membership inference attacks. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 127\u2013137 (2021)","DOI":"10.1145\/3447548.3467445"},{"key":"6_CR7","unstructured":"Choi, E., Biswal, S., Malin, B., Duke, J., Stewart, W.F., Sun, J.: Generating multi-label discrete patient records using generative adversarial networks. In: Machine Learning for Healthcare Conference, pp. 286\u2013305. PMLR (2017)"},{"key":"6_CR8","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"6_CR9","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"1","key":"6_CR10","doi-asserted-by":"publisher","first-page":"133","DOI":"10.2478\/popets-2019-0008","volume":"2019","author":"J Hayes","year":"2019","unstructured":"Hayes, J., Melis, L., Danezis, G., De Cristofaro, E.: Logan: membership inference attacks against generative models. Proc. Priv. Enhancing Technol. 2019(1), 133\u2013152 (2019)","journal-title":"Proc. Priv. Enhancing Technol."},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"He, Z., Zhang, T., Lee, R.B.: Model inversion attacks against collaborative inference. In: Proceedings of the 35th Annual Computer Security Applications Conference, pp. 148\u2013162 (2019)","DOI":"10.1145\/3359789.3359824"},{"key":"6_CR12","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)"},{"issue":"4","key":"6_CR13","doi-asserted-by":"publisher","first-page":"232","DOI":"10.2478\/popets-2019-0067","volume":"2019","author":"B Hilprecht","year":"2019","unstructured":"Hilprecht, B., H\u00e4rterich, M., Bernau, D.: Monte Carlo and reconstruction membership inference attacks against generative models. Proc. Priv. Enhancing Technol. 2019(4), 232\u2013249 (2019)","journal-title":"Proc. Priv. Enhancing Technol."},{"key":"6_CR14","unstructured":"Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: Workshop on Faces in \u2018Real-Life\u2019 Images: Detection, Alignment, and Recognition (2008)"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Hui, B., Yang, Y., Yuan, H., Burlina, P., Gong, N.Z., Cao, Y.: Practical blind membership inference attack via differential comparisons. In: ISOC Network and Distributed System Security Symposium (NDSS) (2021)","DOI":"10.14722\/ndss.2021.24293"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Kai, Y., Yuanqing, L., Lafferty, J.: Learning image representations from the pixel level via hierarchical sparse coding. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1713\u20131720 (2011)","DOI":"10.1109\/CVPR.2011.5995732"},{"key":"6_CR17","unstructured":"Kingma, D., Ba, L., et\u00a0al.: Adam: a method for stochastic optimization (2015)"},{"key":"6_CR18","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Master\u2019s thesis, Department of Computer Science, University of Toronto (2009)"},{"key":"6_CR19","unstructured":"Leino, K., Fredrikson, M.: Stolen memories: leveraging model memorization for calibrated $$\\{$$White-Box$$\\}$$ membership inference. In: 29th USENIX security symposium (USENIX Security 2020), pp. 1605\u20131622 (2020)"},{"key":"6_CR20","unstructured":"Li, C., et al.: Alice: towards understanding adversarial learning for joint distribution matching. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"issue":"1","key":"6_CR21","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1109\/18.61115","volume":"37","author":"J Lin","year":"1991","unstructured":"Lin, J.: Divergence measures based on the shannon entropy. IEEE Trans. Inf. Theory 37(1), 145\u2013151 (1991)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"6_CR22","unstructured":"Ling, C.X., Huang, J., Zhang, H., et\u00a0al.: AUC: a statistically consistent and more discriminating measure than accuracy. In: IJCAI, vol.\u00a03, pp. 519\u2013524 (2003)"},{"key":"6_CR23","unstructured":"Metz, L., Poole, B., Pfau, D., Sohl-Dickstein, J.: Unrolled generative adversarial networks. arXiv preprint arXiv:1611.02163 (2016)"},{"issue":"3","key":"6_CR24","doi-asserted-by":"publisher","first-page":"142","DOI":"10.2478\/popets-2021-0041","volume":"2021","author":"S Mukherjee","year":"2021","unstructured":"Mukherjee, S., Xu, Y., Trivedi, A., Patowary, N., Ferres, J.L.: privGAN: protecting GANs from membership inference attacks at low cost to utility. Proc. Priv. Enhancing Technol. 2021(3), 142\u2013163 (2021)","journal-title":"Proc. Priv. Enhancing Technol."},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Nasr, M., Shokri, R., Houmansadr, A.: Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning. In: 2019 IEEE Symposium on Security and Privacy (SP), pp. 739\u2013753. IEEE (2019)","DOI":"10.1109\/SP.2019.00065"},{"key":"6_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1007\/978-3-319-66179-7_48","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"D Nie","year":"2017","unstructured":"Nie, D., et al.: Medical image synthesis with context-aware generative adversarial networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 417\u2013425. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_48"},{"key":"6_CR27","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"key":"6_CR28","doi-asserted-by":"crossref","unstructured":"Salem, A., Zhang, Y., Humbert, M., Fritz, M., Backes, M.: ML-leaks: model and data independent membership inference attacks and defenses on machine learning models. In: Network and Distributed Systems Security Symposium 2019. Internet Society (2019)","DOI":"10.14722\/ndss.2019.23119"},{"key":"6_CR29","doi-asserted-by":"crossref","unstructured":"Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 3\u201318. IEEE (2017)","DOI":"10.1109\/SP.2017.41"},{"key":"6_CR30","doi-asserted-by":"crossref","unstructured":"Thanh-Tung, H., Tran, T.: Catastrophic forgetting and mode collapse in GANs. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u201310. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207181"},{"issue":"4","key":"6_CR31","doi-asserted-by":"publisher","first-page":"784","DOI":"10.1137\/1118101","volume":"18","author":"S Vallender","year":"1974","unstructured":"Vallender, S.: Calculation of the Wasserstein distance between probability distributions on the line. Theory Probab. Appl. 18(4), 784\u2013786 (1974)","journal-title":"Theory Probab. Appl."},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Yeom, S., Giacomelli, I., Fredrikson, M., Jha, S.: Privacy risk in machine learning: analyzing the connection to overfitting. In: 2018 IEEE 31st Computer Security Foundations Symposium (CSF), pp. 268\u2013282. IEEE (2018)","DOI":"10.1109\/CSF.2018.00027"},{"key":"6_CR33","doi-asserted-by":"crossref","unstructured":"Ying, X.: An overview of overfitting and its solutions. In: Journal of Physics: Conference Series, vol.\u00a01168, p. 022022. IOP Publishing (2019)","DOI":"10.1088\/1742-6596\/1168\/2\/022022"}],"container-title":["Lecture Notes in Computer Science","Information Security Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-1624-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,4]],"date-time":"2025-02-04T06:54:08Z","timestamp":1738652048000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-1624-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819616237","9789819616244"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-1624-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"5 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Security Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"21 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wisa2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wisa.or.kr\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}