{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:24:46Z","timestamp":1772907886759,"version":"3.50.1"},"reference-count":78,"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":[{"name":"National Science Foundation for Distinguished Young Scholars of China","award":["62425201"],"award-info":[{"award-number":["62425201"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171248"],"award-info":[{"award-number":["62171248"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62301189"],"award-info":[{"award-number":["62301189"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176269"],"award-info":[{"award-number":["62176269"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2023YFB2703700"],"award-info":[{"award-number":["2023YFB2703700"]}]},{"name":"Shenzhen Science and Technology Program","award":["KJZD20240903103702004"],"award-info":[{"award-number":["KJZD20240903103702004"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20220818101012025"],"award-info":[{"award-number":["JCYJ20220818101012025"]}]},{"name":"Shenzhen Science and Technology Program","award":["GXWD20220811172936001"],"award-info":[{"award-number":["GXWD20220811172936001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tifs.2025.3589127","type":"journal-article","created":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T17:41:55Z","timestamp":1752601315000},"page":"7648-7662","source":"Crossref","is-referenced-by-count":2,"title":["GI-NAS: Boosting Gradient Inversion Attacks Through Adaptive Neural Architecture Search"],"prefix":"10.1109","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8077-9487","authenticated-orcid":false,"given":"Wenbo","family":"Yu","sequence":"first","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0271-6579","authenticated-orcid":false,"given":"Hao","family":"Fang","sequence":"additional","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4798-230X","authenticated-orcid":false,"given":"Bin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3653-5894","authenticated-orcid":false,"given":"Xiaohang","family":"Sui","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7048-3445","authenticated-orcid":false,"given":"Chuan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9034-4330","authenticated-orcid":false,"given":"Hao","family":"Wu","sequence":"additional","affiliation":[{"name":"Shenzhen ShenNong Information Technology Company Ltd., Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8639-982X","authenticated-orcid":false,"given":"Shu-Tao","family":"Xia","sequence":"additional","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2587-8517","authenticated-orcid":false,"given":"Ke","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","volume":"54","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2988575"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106775"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s12273-021-0871-y"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_1"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00023"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"ref10","first-page":"16937","article-title":"Inverting gradients-how easy is it to break privacy in federated learning?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Geiping"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00989"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00458"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00473"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3073804"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243834"},{"key":"ref16","article-title":"IDLG: Improved deep leakage from gradients","author":"Zhao","year":"2020","journal-title":"arXiv:2001.02610"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01607"},{"key":"ref18","first-page":"29898","article-title":"Gradient inversion with generative image prior","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jeon"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2765202"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00984"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref23","first-page":"7232","article-title":"Evaluating gradient inversion attacks and defenses in federated learning","volume-title":"Proc. Annu. Conf. Neural Inf. Process. Syst. (NeurIPS)","author":"Huang"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/791"},{"key":"ref25","article-title":"Differentially private federated learning: A client level perspective","author":"Geyer","year":"2017","journal-title":"arXiv:1712.07557"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS51616.2021.00081"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2014.2368363"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3293417"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1045"},{"key":"ref30","first-page":"1","article-title":"Deep gradient compression: Reducing the communication bandwidth for distributed training","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Lin"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00919"},{"key":"ref32","first-page":"4771","article-title":"Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Suganuma"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9413080"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00934"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00017"},{"key":"ref36","first-page":"1","article-title":"Neural architecture search with reinforcement learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zoph"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3340915"},{"key":"ref38","article-title":"Survey on the attention based RNN model and its applications in computer vision","author":"Wang","year":"2016","journal-title":"arXiv:1601.06823"},{"key":"ref39","article-title":"Hierarchical representations for efficient architecture search","author":"Liu","year":"2017","journal-title":"arXiv:1711.00436"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/b978-0-323-96104-2.00002-6"},{"key":"ref41","first-page":"2342","article-title":"An empirical exploration of recurrent network architectures","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Jozefowicz"},{"key":"ref42","first-page":"115","article-title":"Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures","volume-title":"Proc. 30th Int. Conf. Mach. Learn.","volume":"28","author":"Bergstra"},{"key":"ref43","first-page":"58","article-title":"Towards automatically-tuned neural networks","volume-title":"Proc. Workshop Autom. Mach. Learn.","author":"Mendoza"},{"key":"ref44","first-page":"3460","article-title":"Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves","volume-title":"Proc. 24th Int. Joint Conf. Artif. Intell.","author":"Domhan"},{"key":"ref45","article-title":"Designing neural network architectures using reinforcement learning","author":"Baker","year":"2016","journal-title":"arXiv:1611.02167"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11709"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00257"},{"key":"ref48","article-title":"SNAS: Stochastic neural architecture search","author":"Xie","year":"2018","journal-title":"arXiv:1812.09926"},{"key":"ref49","article-title":"ProxylessNAS: Direct neural architecture search on target task and hardware","author":"Cai","year":"2018","journal-title":"arXiv:1812.00332"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_22"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992696"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992698"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-031219-041220"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01062"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3378781"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.142"},{"key":"ref57","article-title":"R-GAP: Recursive gradient attack on privacy","author":"Zhu","year":"2020","journal-title":"arXiv:2010.07733"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58523-5_26"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00200"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref62","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref63","first-page":"1","article-title":"Large scale GAN training for high fidelity natural image synthesis","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Brock"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref65","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.2307\/2332226"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-211-89836-9_1025"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dyq191"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01140"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00559"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.2118\/18761-MS"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2023.3302161"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1145\/3625558"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2025.3558581"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24434"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/10810755\/11080068.pdf?arnumber=11080068","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T12:43:54Z","timestamp":1766061834000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11080068\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":78,"URL":"https:\/\/doi.org\/10.1109\/tifs.2025.3589127","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}