{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T20:05:44Z","timestamp":1785182744774,"version":"3.55.0"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"15","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2023YFB2704903"],"award-info":[{"award-number":["2023YFB2704903"]}]},{"name":"Research and Development Program of Department of Industry and Information Technology in Xinjiang Autonomous District","award":["SA0304173"],"award-info":[{"award-number":["SA0304173"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1109\/jiot.2026.3689133","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T19:48:59Z","timestamp":1777578539000},"page":"32611-32623","source":"Crossref","is-referenced-by-count":0,"title":["SFBD: Backdoor Detection via Sequential Fingerprinting of Neural Networks for Securing the IoT Model Supply Chain"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7942-7125","authenticated-orcid":false,"given":"Haiyang","family":"Yu","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5044-7321","authenticated-orcid":false,"given":"Fan","family":"Hong","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian","family":"Xie","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5898-7317","authenticated-orcid":false,"given":"Futai","family":"Zou","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4530-5118","authenticated-orcid":false,"given":"Ping","family":"Yi","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6107-7859","authenticated-orcid":false,"given":"Yue","family":"Wu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.3390\/ai5040112"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.32620\/reks.2024.1.10"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICC52391.2025.11161037"},{"key":"ref4","first-page":"15646","article-title":"Demystifying poisoning backdoor attacks from a statistical perspective","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Wang"},{"key":"ref5","article-title":"Unraveling attacks in machine learning-based IoT ecosystems: A survey and the open libraries behind them","author":"Liu","year":"2024","journal-title":"arXiv:2401.11723"},{"key":"ref6","article-title":"BadNets: Identifying vulnerabilities in the machine learning model supply chain","author":"Gu","year":"2017","journal-title":"arXiv:1708.06733"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58607-2_11"},{"key":"ref8","article-title":"WaNet-imperceptible warping-based backdoor attack","author":"Nguyen","year":"2021","journal-title":"arXiv:2102.10369"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612476"},{"key":"ref10","first-page":"1505","article-title":"Blind backdoors in deep learning models","volume-title":"Proc. 30th USENIX Secur. Symp. (USENIX Secur.)","author":"Bagdasaryan"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2024.24450"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20065-6_7"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.52202\/079017-0753"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3704725"},{"key":"ref15","first-page":"14900","article-title":"Anti-backdoor learning: Training clean models on poisoned data","volume-title":"Proc. NIPS","volume":"34","author":"Li"},{"key":"ref16","article-title":"Backdoor defense via decoupling the training process","author":"Huang","year":"2022","journal-title":"arXiv:2202.03423"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2637"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00470-5_13"},{"key":"ref19","first-page":"16913","article-title":"Adversarial neuron pruning purifies backdoored deep models","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Wu"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00031"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0708"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/647"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/SP40001.2021.00034"},{"key":"ref24","first-page":"6399","article-title":"FreeEagle: Detecting complex neural trojans in data-free cases","volume-title":"Proc. 32nd USENIX Secur. Symp. (USENIX Secur.)","author":"Fu"},{"key":"ref25","first-page":"132545","article-title":"Scanning trojaned models using out-of-distribution samples","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ansari"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/SP54263.2024.00015"},{"key":"ref27","first-page":"38013","article-title":"UMD: Unsupervised model detection for X2X backdoor attacks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xiang"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681075"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2025.240179"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3658644.3690337"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2019.23415"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20065-6_11"},{"key":"ref33","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2012.02.016"},{"issue":"7","key":"ref35","first-page":"3","article-title":"Tiny imagenet visual recognition challenge","volume":"7","author":"Le","year":"2015","journal-title":"CS 231N"},{"key":"ref36","article-title":"Revisiting the assumption of latent separability for backdoor defenses","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Qi"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01465"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2019.8802997"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3359789.3359790"},{"key":"ref40","first-page":"18992","article-title":"IBD-PSC: Input-level backdoor detection via parameter-oriented scaling consistency","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","volume":"235","author":"Hou"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58592-1_14"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3632"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548261"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10096332"},{"key":"ref45","first-page":"57","article-title":"Can you hear it? Backdoor attacks via ultrasonic triggers","volume-title":"Proc. ACM Workshop Wireless Secur. Mach. Learn.","author":"Koffas"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709385"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/11623239\/11501731.pdf?arnumber=11501731","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T19:16:25Z","timestamp":1785179785000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11501731\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":46,"journal-issue":{"issue":"15"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2026.3689133","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8]]}}}