{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:52:45Z","timestamp":1779382365695,"version":"3.53.1"},"reference-count":31,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","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\/501100001381","name":"National Research Foundation Singapore through AI Singapore Program","doi-asserted-by":"publisher","award":["AISG2-GC-2023-007"],"award-info":[{"award-number":["AISG2-GC-2023-007"]}],"id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1109\/tnnls.2024.3383722","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T18:19:18Z","timestamp":1712773158000},"page":"21-31","source":"Crossref","is-referenced-by-count":11,"title":["Generative Image Reconstruction From Gradients"],"prefix":"10.1109","volume":"36","author":[{"given":"Ekanut","family":"Sotthiwat","sequence":"first","affiliation":[{"name":"Department of Computer Science, National University of Singapore, Queenstown, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0481-3298","authenticated-orcid":false,"given":"Liangli","family":"Zhen","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing, Agency for Science Technology and Research (A*STAR), Connexis North Tower, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5735-4454","authenticated-orcid":false,"given":"Chi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing, Agency for Science Technology and Research (A*STAR), Connexis North Tower, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1462-9905","authenticated-orcid":false,"given":"Zengxiang","family":"Li","sequence":"additional","affiliation":[{"name":"ENN Group, ENNEW Digital Research Institute, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9116-1595","authenticated-orcid":false,"given":"Rick Siow Mong","family":"Goh","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing, Agency for Science Technology and Research (A*STAR), Connexis North Tower, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Federated learning: Strategies for improving communication efficiency","author":"Kone\u010dn\u00fd","year":"2016","journal-title":"arXiv:1610.05492"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134012"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"ref7","article-title":"IDLG: Improved deep leakage from gradients","author":"Zhao","year":"2020","journal-title":"arXiv:2001.02610"},{"key":"ref8","first-page":"16937","article-title":"Inverting gradients\u2014How easy is it to break privacy in federated learning?","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Geiping"},{"key":"ref9","first-page":"29898","article-title":"Gradient inversion with generative image prior","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Jeon"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.5555\/3016100.3016102"},{"key":"ref11","first-page":"6106","article-title":"Poison frogs! Targeted clean-label poisoning attacks on neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Shafahi"},{"key":"ref12","article-title":"BadNets: Identifying vulnerabilities in the machine learning model supply chain","author":"Gu","year":"2017","journal-title":"arXiv:1708.06733"},{"key":"ref13","first-page":"1467","article-title":"Poisoning attacks against support vector machines","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Biggio"},{"key":"ref14","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Blanchard"},{"key":"ref15","article-title":"Mitigating Sybils in federated learning poisoning","author":"Fung","year":"2018","journal-title":"arXiv:1808.04866"},{"key":"ref16","first-page":"1","article-title":"DBA: Distributed backdoor attacks against federated learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Xie"},{"key":"ref17","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. 23rd Int. Conf. Artif. Intell. Stat.","author":"Bagdasaryan"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148790"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCN49398.2020.9209744"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00023"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01607"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref25","volume-title":"The CIFAR-10 Dataset","author":"Krizhevsky","year":"2009"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.369"},{"key":"ref28","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kingma"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2109730"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3626235"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10832116\/10495167.pdf?arnumber=10495167","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T20:04:29Z","timestamp":1736971469000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10495167\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":31,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2024.3383722","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]}}}