{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:19:03Z","timestamp":1742930343631,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819724574"},{"type":"electronic","value":"9789819724581"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-2458-1_7","type":"book-chapter","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T04:01:50Z","timestamp":1713758510000},"page":"90-103","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Client-Side Watermarking with\u00a0Private-Class in\u00a0Federated Learning"],"prefix":"10.1007","author":[{"given":"Weitong","family":"Chen","sequence":"first","affiliation":[]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jiale","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaobing","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Xiang","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Chengcheng","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,23]]},"reference":[{"key":"7_CR1","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y\u00a0Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Singh, A., Zhu, X.J. (eds.) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, vol.\u00a054, pp. 1273\u20131282. PMLR (2017). arXiv: 1602.05629"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Wu, D., Wang, N., Zhang, J., Zhang, Y., Xiang, Y., Gao, L.: A blockchain-based multi-layer decentralized framework for robust federated learning. In: International Joint Conference on Neural Networks, pp.\u00a01\u20138. IEEE (2022)","DOI":"10.1109\/IJCNN55064.2022.9892039"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., Backes, M.: Ml-leaks: model and data independent membership inference attacks and defenses on machine learning models. In: 26th Annual Network and Distributed System Security Symposium. The Internet Society (2019). arXiv: 1806.01246","DOI":"10.14722\/ndss.2019.23119"},{"issue":"3","key":"7_CR4","first-page":"2341","volume":"20","author":"L Liu","year":"2023","unstructured":"Liu, L., Wang, Y., Liu, G., Peng, K., Wang, C.: Membership inference attacks against machine learning models via prediction sensitivity. IEEE Trans. Dependable Secur. Comput. 20(3), 2341\u20132347 (2023)","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"key":"7_CR5","unstructured":"Yan, M., Fletcher, C.W., Torrellas, J.: Cache telepathy: leveraging shared resource attacks to learn DNN architectures. In: Capkun, S., Roesner, F. (eds.) 29th USENIX Security Symposium, pp. 2003\u20132020. USENIX Association (2020), arXiv: 1808.04761"},{"key":"7_CR6","unstructured":"Jagielski, M., Carlini, N., Berthelot, D., Kurakin, A., Papernot, N.: High accuracy and high fidelity extraction of neural networks. In: Capkun, S., Roesner, F. (eds.) 29th USENIX Security Symposium, pp. 1345\u20131362. USENIX Association (2020). arxiv.org\/abs\/1909.01838"},{"key":"7_CR7","unstructured":"Zhu, Y., Cheng, Y., Zhou, H., Lu, Y.: Hermes attack: steal dnn models with loss less inference accuracy. In: Bailey, M., Greenstadt, R. (eds.) 30th USENIX Security Symposium, pp. 1973\u20131988. USENIX Association (2021). arxiv.org\/abs\/2006.12784"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Namba, R., Sakuma, J.: Robust watermarking of neural network with exponential weighting. In: Galbraith, S.D., Russello, G., Susilo, W., Gollmann, D., Kirda, E., Liang, Z. (eds.) Asia Conference on Computer and Communications Security, pp. 228\u2013240. ACM (2019)","DOI":"10.1145\/3321705.3329808"},{"issue":"10","key":"7_CR9","doi-asserted-by":"publisher","first-page":"6122","DOI":"10.1109\/TPAMI.2021.3088846","volume":"44","author":"L Fan","year":"2022","unstructured":"Fan, L., Ng, K.W., Chan, C.S., Yang, Q.: Deepipr: deep neural network ownership verification with passports. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6122\u20136139 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Szyller, S., Atli, B.G., Marchal, S., Asokan, N.: DAWN: dynamic adversarial watermarking of neural networks. In: Shen, H.T., Zhuang, Y., Smith, J.R., Yang, Y., C\u00e9sar, P., Metze, F., Prabhakaran, B. (eds.) Multimedia Conference, pp. 4417\u20134425. ACM (2021)","DOI":"10.1145\/3474085.3475591"},{"issue":"2","key":"7_CR11","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1109\/JBHI.2021.3123936","volume":"27","author":"B Han","year":"2023","unstructured":"Han, B., Jhaveri, R.H., Wang, H., Qiao, D., Du, J.: Application of robust zero-watermarking scheme based on federated learning for securing the healthcare data. IEEE J. Biomed. Health Inform. 27(2), 804\u2013813 (2023)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"14","key":"7_CR12","doi-asserted-by":"publisher","first-page":"16497","DOI":"10.1007\/s10489-022-03339-0","volume":"52","author":"M Xue","year":"2022","unstructured":"Xue, M., Sun, S., Zhang, Y., Wang, J., Liu, W.: Active intellectual property protection for deep neural networks through stealthy backdoor and users\u2019 identities authentication. Appl. Intell. 52(14), 16497\u201316511 (2022)","journal-title":"Appl. Intell."},{"key":"7_CR13","unstructured":"Jia, H., Choquette-Choo, C.A., Chandrasekaran, V., Papernot, N.: Entangled watermarks as a defense against model extraction. In: Bailey, M., Greenstadt, R. (eds.) 30th USENIX Security Symposium, pp. 1937\u20131954. USENIX Association (2021). arXiv: 2002.12200"},{"key":"7_CR14","unstructured":"Rouhani, B.D., Chen, H., Koushanfar, F.: Deepsigns: an end-to-end watermarking framework for ownership protection of deep neural networks. In: Bahar, I., Herlihy, M., Witchel, E., Lebeck, A.R. (eds.) Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 485\u2013497. ACM (2019)"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Li, F., Wang, S., Liew, A.W.: Watermarking protocol for deep neural network ownership regulation in federated learning. In: International Conference on Multimedia and Expo Workshops, pp.\u00a01\u20134. IEEE (2022)","DOI":"10.1109\/ICMEW56448.2022.9859395"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Tekgul, B.G.A., Xia, Y., Marchal, S., Asokan, N.: WAFFLE: watermarking in federated learning. In: 40th International Symposium on Reliable Distributed Systems, pp. 310\u2013320. IEEE (2021)","DOI":"10.1109\/SRDS53918.2021.00038"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"34 Liu, X., Shao, S., Yang, Y., Wu, K., Yang, W., Fang, H.: Secure federated learning model verification: a client-side backdoor triggered watermarking scheme. In: International Conference on Systems, Man, and Cybernetics, pp. 2414\u20132419. IEEE (2021)","DOI":"10.1109\/SMC52423.2021.9658998"},{"key":"7_CR18","unstructured":"Adi, Y., Baum, C., Ciss\u00e9, M., Pinkas, B., Keshet, J.: Turning your weakness into a strength: Watermarking deep neural networks by backdooring. In: Enck, W., Felt, A.P. (eds.) 27th USENIX Security Symposium, pp. 1615\u20131631. USENIX Association (2018)"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Maung, A.P.M., Kiya, H.: Piracy-resistant DNN watermarking by block-wise image transformation with secret key. In: Borghys, D., Bas, P., Verdoliva, L., Pevn\u00fd, T., Li, B., Newman, J. (eds.) IACM Workshop on Information Hiding and Multimedia Security, pp. 159\u2013164. ACM (2021)","DOI":"10.1145\/3437880.3460398"},{"key":"7_CR20","unstructured":"Yan, Y., Pan, X., Zhang, M., Yang, M.: Rethinking white-box watermarks on deep learning models under neural structural obfuscation. In: Calandrino, J.A., Troncoso, C. (eds.) 32nd USENIX Security Symposium. USENIX Association (2023)"},{"key":"7_CR21","unstructured":"Fan, L., Ng, K.W., Chan, C.S.: Rethinking deep neural network ownership verification: embedding passports to defeat ambiguity attacks. In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E.B., Garnett, R. (eds.) Advances in Neural Information Processing Systems, pp. 4716\u20134725 (2019)"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Uchida, Y., Nagai, Y., Sakazawa, S., Satoh, S.: Embedding watermarks into deep neural networks. In: Ionescu, B., Sebe, N., Feng, J., Larson, M.A., Lienhart, R., Snoek, C. (eds.) ACM on International Conference on Multimedia Retrieval, pp. 269\u2013277. ACM (2017)","DOI":"10.1145\/3078971.3078974"},{"issue":"10","key":"7_CR23","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"7_CR24","unstructured":"Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. In: 5th International Conference on Learning Representations. OpenReview.net (2017)"},{"key":"7_CR25","unstructured":"LeCun, Y., Corinna\u00a0Cortes, C.J.B.: Mnist handwritten digit database. ATT Labs (2010)"},{"key":"7_CR26","unstructured":"Alex\u00a0Krizhevsky, G.H.: Learning multiple layers of features from tiny images. Tech Report (2009)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-2458-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T19:07:27Z","timestamp":1731784047000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-2458-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819724574","9789819724581"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-2458-1_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"23 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Yanuca Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fiji","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}