{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T07:20:06Z","timestamp":1763018406816,"version":"3.37.3"},"reference-count":59,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province of China","doi-asserted-by":"publisher","award":["2021JM-216","2021JQ-335"],"award-info":[{"award-number":["2021JM-216","2021JQ-335"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3110239","type":"journal-article","created":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T20:06:28Z","timestamp":1630699588000},"page":"127204-127216","source":"Crossref","is-referenced-by-count":10,"title":["Use Procedural Noise to Achieve Backdoor Attack"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0061-8148","authenticated-orcid":false,"given":"Xuan","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuena","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiwei","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"13195","article-title":"TBT: Targeted neural network attack with bit trojan","author":"rakin","year":"2020","journal-title":"Proc IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref38","first-page":"1","article-title":"Backdooring convolutional neural networks via targeted weight perturbations","author":"dumford","year":"2020","journal-title":"Proc IEEE Int Joint Conf Biometrics (IJCB)"},{"doi-asserted-by":"publisher","key":"ref33","DOI":"10.1109\/EuroSP48549.2020.00019"},{"doi-asserted-by":"publisher","key":"ref32","DOI":"10.1109\/SP.2019.00031"},{"key":"ref31","article-title":"Detecting backdoor attacks on deep neural networks by activation clustering","author":"chen","year":"2018","journal-title":"arXiv 1811 03728"},{"key":"ref30","first-page":"1","article-title":"Spectral signatures in backdoor attacks","author":"tran","year":"2018","journal-title":"Proc NeurIPS"},{"key":"ref37","article-title":"Hidden backdoor attack against semantic segmentation models","author":"li","year":"2021","journal-title":"arXiv 2103 04038"},{"key":"ref36","first-page":"113","article-title":"Composite backdoor attack for deep neural network by mixing existing benign features","author":"lin","year":"2020","journal-title":"Proc ACM SIGSAC Conf Comput Commun Secur"},{"key":"ref35","article-title":"Input-aware dynamic backdoor attack","author":"nguyen","year":"2020","journal-title":"arXiv 2010 08138"},{"key":"ref34","article-title":"Dynamic backdoor attacks against machine learning models","author":"salem","year":"2020","journal-title":"arXiv 2003 03675"},{"year":"2018","author":"turner","article-title":"Clean-label backdoor attacks","key":"ref28"},{"key":"ref27","article-title":"Graph backdoor","author":"xi","year":"2020","journal-title":"arXiv 2006 11890"},{"key":"ref29","first-page":"1","article-title":"Poison frogs! targeted clean-label poisoning attacks on neural networks","author":"shafahi","year":"2018","journal-title":"Proc NeurIPS"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1007\/s11023-020-09548-1"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"krizhevsky","year":"2012","journal-title":"Commun ACM"},{"key":"ref20","article-title":"Targeted backdoor attacks on deep learning systems using data poisoning","author":"chen","year":"2017","journal-title":"arXiv 1712 05526"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1007\/978-3-030-58607-2_11"},{"doi-asserted-by":"publisher","key":"ref21","DOI":"10.1109\/ICIP.2019.8802997"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1145\/3374664.3375751"},{"key":"ref23","article-title":"WaNet&#x2013;imperceptible warping-based backdoor attack","author":"nguyen","year":"2021","journal-title":"arXiv 2102 10369"},{"key":"ref26","first-page":"1","article-title":"Poison attacks against text datasets with conditional adversarially regularized autoencoder","author":"chan","year":"2020","journal-title":"Proc Findings Assoc for Comput Linguistics"},{"doi-asserted-by":"publisher","key":"ref25","DOI":"10.1109\/CVPR42600.2020.01445"},{"key":"ref50","article-title":"Backdoor attacks and countermeasures on deep learning: A comprehensive review","author":"gao","year":"2020","journal-title":"arXiv 2007 10760"},{"key":"ref51","first-page":"1","article-title":"State of the art in procedural noise functions","author":"lagae","year":"2010","journal-title":"Proc Eurographics"},{"key":"ref59","article-title":"Benchmarking neural network robustness to common corruptions and surface variations","author":"hendrycks","year":"2018","journal-title":"arXiv 1807 01697"},{"doi-asserted-by":"publisher","key":"ref58","DOI":"10.1007\/s11263-019-01228-7"},{"doi-asserted-by":"publisher","key":"ref57","DOI":"10.1109\/EuroSP.2016.36"},{"doi-asserted-by":"publisher","key":"ref56","DOI":"10.1145\/3319535.3345660"},{"doi-asserted-by":"publisher","key":"ref55","DOI":"10.1145\/237170.237267"},{"doi-asserted-by":"publisher","key":"ref54","DOI":"10.1145\/1531326.1531360"},{"year":"2015","author":"mitrovic","article-title":"Real-time shading languages","key":"ref53"},{"doi-asserted-by":"publisher","key":"ref52","DOI":"10.1145\/325165.325247"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.14722\/ndss.2018.23291"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1145\/2810103.2813677"},{"key":"ref40","article-title":"Targeted attack against deep neural networks via flipping limited weight bits","author":"bai","year":"2021","journal-title":"arXiv 2102 10496"},{"key":"ref12","article-title":"Label-only membership inference attacks","author":"choquette-choo","year":"2020","journal-title":"arXiv 2007 14321"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1145\/2976749.2978392"},{"key":"ref14","article-title":"Stabilized medical image attacks","author":"qi","year":"2021","journal-title":"arXiv 2103 05232"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1016\/j.heliyon.2019.e01802"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1109\/CVPR42600.2020.00108"},{"key":"ref17","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proc AISTATS"},{"key":"ref18","first-page":"2938","article-title":"How to backdoor federated learning","author":"bagdasaryan","year":"2020","journal-title":"Proc AISTATS"},{"year":"2020","author":"wang","journal-title":"arXiv 2007 05084","key":"ref19"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1007\/s13246-020-00865-4"},{"key":"ref3","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016","journal-title":"arXiv 1609 02907"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1016\/j.chaos.2020.110059"},{"key":"ref5","article-title":"Deep learning-based detection for COVID-19 from chest ct using weak label","author":"zheng","year":"2020","journal-title":"medRxiv"},{"key":"ref8","first-page":"1","article-title":"Explaining and harnessing adversarial examples","volume":"abs 1412 6572","author":"goodfellow","year":"2015","journal-title":"CoRR"},{"key":"ref7","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"2013","journal-title":"arXiv 1312 6199"},{"key":"ref49","article-title":"REGularization can helpmitigatepoisoningattacks&#x2026;with therighthyperparameters","author":"carnerero-cano","year":"2021","journal-title":"arXiv 2105 10948"},{"key":"ref9","article-title":"BadNets: Identifying vulnerabilities in the machine learning model supply chain","author":"gu","year":"2017","journal-title":"arXiv 1708 06733"},{"doi-asserted-by":"publisher","key":"ref46","DOI":"10.1007\/978-3-030-00470-5_13"},{"key":"ref45","article-title":"Rethinking the backdoor attacks&#x2019; triggers: A frequency perspective","author":"zeng","year":"2021","journal-title":"arXiv 2104 03413"},{"key":"ref48","article-title":"What doesn&#x2019;t kill you makes you robust (er): Adversarial training against poisons and backdoors","author":"geiping","year":"2021","journal-title":"arXiv 2102 13624"},{"key":"ref47","article-title":"Rethinking the trigger of backdoor attack","author":"li","year":"2020","journal-title":"arXiv 2004 04692"},{"key":"ref42","article-title":"Demon in the variant: Statistical analysis of DNNs for robust backdoor contamination detection","author":"tang","year":"2019","journal-title":"arXiv 1908 00686"},{"key":"ref41","article-title":"Poisoning and backdooring contrastive learning","author":"carlini","year":"2021","journal-title":"arXiv 2106 09667"},{"key":"ref44","article-title":"TOP: Backdoor detection in neural networks via transferability of perturbation","author":"huster","year":"2021","journal-title":"arXiv 2103 10274"},{"doi-asserted-by":"publisher","key":"ref43","DOI":"10.1145\/3359789.3359790"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09529206.pdf?arnumber=9529206","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:55:27Z","timestamp":1639770927000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9529206\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":59,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3110239","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2021]]}}}