{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T05:01:27Z","timestamp":1764997287010},"reference-count":13,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,12,1]],"date-time":"2019-12-01T00:00:00Z","timestamp":1575158400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1109\/globecom38437.2019.9013408","type":"proceedings-article","created":{"date-parts":[[2020,2,28]],"date-time":"2020-02-28T09:59:24Z","timestamp":1582883964000},"page":"1-6","source":"Crossref","is-referenced-by-count":7,"title":["Towards Robust Ensemble Defense Against Adversarial Examples Attack"],"prefix":"10.1109","author":[{"given":"Nag","family":"Mani","sequence":"first","affiliation":[]},{"given":"Melody","family":"Moh","sequence":"additional","affiliation":[]},{"given":"Teng-Sheng","family":"Moh","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"journal-title":"Towards deep neural network architectures robust to adversarial examples","year":"2014","author":"gu","key":"ref10"},{"journal-title":"Towards deep learning models resistant to adversarial attacks","year":"2017","author":"madry","key":"ref11"},{"journal-title":"Adversarial machine learning at scale","year":"2016","author":"kurakin","key":"ref12"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-010-0007-7"},{"journal-title":"Intriguing properties of neural networks","year":"2013","author":"szegedy","key":"ref6"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014066"},{"journal-title":"Distillation as a defense to adversarial perturbations against deep neural networks","year":"2015","author":"papernot","key":"ref8"},{"journal-title":"Explaining and Harnessing Adversarial Examples","year":"2014","author":"goodfellow","key":"ref7"},{"journal-title":"Adversarial examples in the physical world","year":"2016","author":"kurakin","key":"ref2"},{"key":"ref1","first-page":"39","article-title":"Adversarial Attacks and Defense on Deep Learning Models for Big Data and IoT","author":"mani","year":"2019","journal-title":"Handbook of Research on Cloud Computing and Big Data Applications in IoT"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"}],"event":{"name":"GLOBECOM 2019 - 2019 IEEE Global Communications Conference","start":{"date-parts":[[2019,12,9]]},"location":"Waikoloa, HI, USA","end":{"date-parts":[[2019,12,13]]}},"container-title":["2019 IEEE Global Communications Conference (GLOBECOM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8968653\/9013108\/09013408.pdf?arnumber=9013408","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,17]],"date-time":"2022-07-17T21:54:18Z","timestamp":1658094858000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9013408\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12]]},"references-count":13,"URL":"https:\/\/doi.org\/10.1109\/globecom38437.2019.9013408","relation":{},"subject":[],"published":{"date-parts":[[2019,12]]}}}