{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T05:58:38Z","timestamp":1762408718490,"version":"build-2065373602"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001866","name":"Luxembourg National Research Fund","doi-asserted-by":"publisher","award":["13550291"],"award-info":[{"award-number":["13550291"]}],"id":[{"id":"10.13039\/501100001866","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1109\/tai.2025.3560921","type":"journal-article","created":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T13:54:53Z","timestamp":1744811693000},"page":"3018-3029","source":"Crossref","is-referenced-by-count":0,"title":["Ownership Infringement Detection for Generative Adversarial Networks Against Model Stealing"],"prefix":"10.1109","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5138-4014","authenticated-orcid":false,"given":"Hailong","family":"Hu","sequence":"first","affiliation":[{"name":"Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Esch-sur-Alzette, Luxembourg"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4521-4112","authenticated-orcid":false,"given":"Jun","family":"Pang","sequence":"additional","affiliation":[{"name":"Faculty of Science, Technology and Medicine and the Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, Esch-sur-Alzette, Luxembourg"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst. (NeurIPS)","volume":"33","author":"Brown","year":"2020"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3528233.3530738"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00158"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3181070"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-4048(02)01009-X"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3485832.3485838"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00363"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/WIFS55849.2022.9975409"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2023.103102"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01418"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/MIPR.2019.00103"},{"key":"ref14","article-title":"Progressive growing of GANs for quality, stability, and variation","author":"Karras","year":"2018","journal-title":"Proc. Int. Conf. Learn. Representations (ICLR)"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref16","first-page":"2234","article-title":"Improved techniques for training GANs","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst. (NeurIPS)","author":"Salimans","year":"2016"},{"key":"ref17","first-page":"5769","article-title":"Improved training of Wasserstein GANs","volume-title":"Proc. Annu. Conf. Neural Inform. Process. Syst. (NeurIPS)","author":"Gulrajani","year":"2017"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.05957"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref20","first-page":"7354","article-title":"Self-attention generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Zhang","year":"2019"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2021.3133824"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3078971.3078974"},{"key":"ref23","first-page":"1615","article-title":"Turning your weakness into a strength: Watermarking deep neural networks by backdooring","volume-title":"Proc. USENIX Secur. Symp. (USENIX Secur.).","author":"Adi","year":"2018"},{"key":"ref24","first-page":"1","article-title":"Dataset inference: Ownership resolution in machine learning","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR)","author":"Maini","year":"2020"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/SP46214.2022.9833747"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2024.3351116"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TAI.2024.3388389"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123776"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111675"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-024-05746-x"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00462"},{"key":"ref32","first-page":"1","article-title":"Responsible disclosure of generative models using scalable fingerprinting","volume-title":"Proc. Int. Conf. Learn. Representations (ICLR)","author":"Yu","year":"2022"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00765"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.5244\/C.35.53"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.5555\/3241094.3241142"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-conmatphys-031119-050745"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"article-title":"LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop","year":"2015","author":"Yu","key":"ref38"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref40","first-page":"6626","article-title":"GANs trained by a two time-scale update rule converge to a local Nash equilibrium","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst. (NeurIPS)","author":"Heusel","year":"2017"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/S1364-6613(99)01294-2"},{"article-title":"An empirical investigation of catastrophic forgetting in gradient-based neural networks","year":"2013","author":"Goodfellow","key":"ref44"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/9078688\/11224646\/10966017.pdf?arnumber=10966017","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T05:46:23Z","timestamp":1762407983000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10966017\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":45,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tai.2025.3560921","relation":{},"ISSN":["2691-4581"],"issn-type":[{"type":"electronic","value":"2691-4581"}],"subject":[],"published":{"date-parts":[[2025,11]]}}}