{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T21:05:43Z","timestamp":1784927143835,"version":"3.55.0"},"reference-count":41,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC-Fundo para o Desenvolvimento das Ci\u00eancias e da Tecnologia [Science and Technology Development Fund (FDCT)] under the Joint Scientific Research Project Fund","doi-asserted-by":"publisher","award":["0051\/2022\/AFJ"],"award-info":[{"award-number":["0051\/2022\/AFJ"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tifs.2025.3538266","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T18:34:33Z","timestamp":1739471673000},"page":"2143-2156","source":"Crossref","is-referenced-by-count":17,"title":["QUEEN: Query Unlearning Against Model Extraction"],"prefix":"10.1109","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8489-9386","authenticated-orcid":false,"given":"Huajie","family":"Chen","sequence":"first","affiliation":[{"name":"Faculty of Data Science, City University of Macau, Xian Xing Hai, Macau"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3411-7947","authenticated-orcid":false,"given":"Tianqing","family":"Zhu","sequence":"additional","affiliation":[{"name":"Faculty of Data Science, City University of Macau, Xian Xing Hai, Macau"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7608-7910","authenticated-orcid":false,"given":"Lefeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Data Science, City University of Macau, Xian Xing Hai, Macau"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3603-6617","authenticated-orcid":false,"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1388-7715","authenticated-orcid":false,"given":"Derui","family":"Wang","sequence":"additional","affiliation":[{"name":"CSIRO’s Data61, Eveleigh, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1680-2521","authenticated-orcid":false,"given":"Wanlei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Data Science, City University of Macau, Xian Xing Hai, Macau"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9172-4252","authenticated-orcid":false,"given":"Minhui","family":"Xue","sequence":"additional","affiliation":[{"name":"CSIRO’s Data61, Eveleigh, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"ChatGPT is not all you need. A state of the art review of large generative AI models","author":"Gozalo-Brizuela","year":"2023","journal-title":"arXiv:2301.04655"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/SP46215.2023.10179406"},{"key":"ref3","first-page":"1964","article-title":"Label-only membership inference attacks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Choquette-Choo"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560684"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3559355"},{"key":"ref6","first-page":"1937","article-title":"Entangled watermarks as a defense against model extraction","volume-title":"Proc. USENIX Secur. Symp.","author":"Jia"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01307"},{"key":"ref8","first-page":"36571","article-title":"Are you stealing my model? Sample correlation for fingerprinting deep neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Guan"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01929"},{"key":"ref10","article-title":"Increasing the cost of model extraction with calibrated proof of work","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dziedzic"},{"key":"ref11","article-title":"Defending against machine learning model stealing attacks using deceptive perturbations","author":"Lee","year":"2018","journal-title":"arXiv:1806.00054"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00085"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489592"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00509"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5432"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2020.24178"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00474"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01360"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3265535"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3244107"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.423"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3384846"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2023.3313577"},{"key":"ref25","article-title":"Prediction poisoning: Towards defenses against DNN model stealing attacks","volume-title":"Proc. 8th Int. Conf. Learn. Represent.","author":"Orekondy"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2019.00044"},{"key":"ref27","article-title":"Protecting DNNs from theft using an ensemble of diverse models","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kariyappa"},{"key":"ref28","first-page":"8759","article-title":"Scaling out-of-distribution detection for real-world settings","volume-title":"Proc. 39th Int. Conf. Mach. Learn. (ICML)","author":"Hendrycks"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/3548606.3560675"},{"key":"ref30","first-page":"1","article-title":"MODELGUARD: Information-theoretic defense against model extraction attacks","volume-title":"Proc. 33rd USENIX Secur. Symp. (Secur.)","author":"Tang"},{"key":"ref31","first-page":"18661","article-title":"Supervised contrastive learning","volume-title":"Proc. NIPS","author":"Khosla"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01616"},{"key":"ref33","first-page":"22911","article-title":"Reconstructing training data from trained neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Haim"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00750"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/1968.1972"},{"key":"ref36","volume-title":"Elements of Information Theory","author":"Cover","year":"1999"},{"key":"ref37","first-page":"1345","article-title":"High accuracy and high fidelity extraction of neural networks","volume-title":"Proc. 29th USENIX Conf. Secur. Symp.","author":"Jagielski"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833693"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417269"},{"key":"ref40","article-title":"QUEEN: Query unlearning against model extraction","author":"Chen","year":"2024","journal-title":"arXiv:2407.01251"},{"key":"ref41","article-title":"TOFU: A task of fictitious unlearning for LLMs","author":"Maini","year":"2024","journal-title":"arXiv:2401.06121"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/10810755\/10887027.pdf?arnumber=10887027","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T05:26:20Z","timestamp":1740461180000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10887027\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/tifs.2025.3538266","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}