{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T12:52:37Z","timestamp":1770555157170,"version":"3.49.0"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Monash-Data61 collaborative research project","award":["Data61 CRP43"],"award-info":[{"award-number":["Data61 CRP43"]}]},{"name":"Research Grants Council of Hong Kong","award":["CityU 11217819"],"award-info":[{"award-number":["CityU 11217819"]}]},{"name":"Research Grants Council of Hong Kong","award":["N_CityU139\/21"],"award-info":[{"award-number":["N_CityU139\/21"]}]},{"name":"Research Grants Council of Hong Kong","award":["R6021-20F"],"award-info":[{"award-number":["R6021-20F"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Dependable and Secure Comput."],"published-print":{"date-parts":[[2023,3,1]]},"DOI":"10.1109\/tdsc.2022.3144988","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T20:33:29Z","timestamp":1643142809000},"page":"886-901","source":"Crossref","is-referenced-by-count":6,"title":["Defeating Misclassification Attacks Against Transfer Learning"],"prefix":"10.1109","volume":"20","author":[{"given":"Bang","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Information Technology, Monash University, Clayton, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8938-2364","authenticated-orcid":false,"given":"Shuo","family":"Wang","sequence":"additional","affiliation":[{"name":"CSIRO Data61, Clayton, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3701-4946","authenticated-orcid":false,"given":"Xingliang","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Monash University, Clayton, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0547-315X","authenticated-orcid":false,"given":"Cong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9050-5675","authenticated-orcid":false,"given":"Carsten","family":"Rudolph","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Monash University, Clayton, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangwen","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Monash University, Clayton, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Google cloud AutoML","year":"2019"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3398209"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref5","first-page":"1281","article-title":"With great training comes great vulnerability: Practical attacks against transfer learning","volume-title":"Proc. 27th USENIX Conf. Secur. Symp.","author":"Wang"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243757"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23198"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2019.23415"},{"key":"ref9","article-title":"Ensemble adversarial training: Attacks and defenses","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Tram\u00e8r"},{"key":"ref10","article-title":"Adversarial machine learning at scale","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kurakin"},{"key":"ref11","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Goodfellow"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref13","article-title":"On evaluating adversarial robustness","author":"Carlini","year":"2019"},{"key":"ref14","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","volume-title":"Proc. 28th Int. Conf. Neural Inf. Process. Syst.","author":"Han"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00031"},{"key":"ref16","article-title":"Rethinking the value of network pruning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu"},{"key":"ref17","first-page":"2498","article-title":"Variational dropout sparsifies deep neural networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Molchanov"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2015.2494536"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00470-5_13"},{"key":"ref20","article-title":"Pruning filters for efficient convnets","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2011.5981788"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2011.6033395"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.41"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459250"},{"key":"ref25","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Simonyan"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2012.02.016"},{"key":"ref27","article-title":"ADADELTA: An adaptive learning rate method","author":"Zeiler","year":"2012"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref29","article-title":"Adv-BNN: Improved adversarial defense through robust Bayesian neural network","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Liu"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00894"},{"key":"ref31","article-title":"On adaptive attacks to adversarial example defenses","volume-title":"Proc. 34th Conf. Neural Inf. Process. Syst.","author":"Tram\u00e8r"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3423338"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3196494.3196539"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.3021008"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.2971601"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3134599"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3128572.3140449"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.06083"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.173"},{"key":"ref40","article-title":"Robustness to adversarial examples through an ensemble of specialists","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Abbasi"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2018.8646578"},{"key":"ref42","article-title":"Training ensembles to detect adversarial examples","author":"Bagnall","year":"2017"},{"key":"ref43","article-title":"Improving adversarial robustness of ensembles with diversity training","author":"Kariyappa","year":"2019"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/MASS.2019.00040"},{"key":"ref45","first-page":"4970","article-title":"Improving adversarial robustness via promoting ensemble diversity","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Pang"},{"key":"ref46","article-title":"Adversarial example defense: Ensembles of weak defenses are not strong","volume-title":"Proc. 11th USENIX Workshop Offensive Technol.","author":"He"}],"container-title":["IEEE Transactions on Dependable and Secure Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8858\/10068314\/09693248.pdf?arnumber=9693248","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T22:33:57Z","timestamp":1705185237000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9693248\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,1]]},"references-count":46,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tdsc.2022.3144988","relation":{},"ISSN":["1545-5971","1941-0018","2160-9209"],"issn-type":[{"value":"1545-5971","type":"print"},{"value":"1941-0018","type":"electronic"},{"value":"2160-9209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,1]]}}}