{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T13:31:10Z","timestamp":1776519070252,"version":"3.51.2"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea (NRF) Grants","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Korea Government","doi-asserted-by":"publisher","award":["2021R1I1A1A01040308"],"award-info":[{"award-number":["2021R1I1A1A01040308"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hwarang-Dae Research Institute of Korea Military Academy"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2020.3042839","type":"journal-article","created":{"date-parts":[[2020,12,8]],"date-time":"2020-12-08T00:07:39Z","timestamp":1607386059000},"page":"5345-5356","source":"Crossref","is-referenced-by-count":13,"title":["AdvGuard: Fortifying Deep Neural Networks Against Optimized Adversarial Example Attack"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1169-9892","authenticated-orcid":false,"given":"Hyun","family":"Kwon","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, Korea Military Academy, Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Computer Information and Science, Hoseo University, Asan-si, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"ref2","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Simonyan"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2205597"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ETFA.2016.7733515"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/nature16961"},{"key":"ref6","first-page":"1","article-title":"Intriguing properties of neural networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Szegedy"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2886017"},{"key":"ref8","first-page":"1","article-title":"Explaining and harnessing adversarial examples","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Goodfellow"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.41"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5663-3"},{"key":"ref11","first-page":"1","article-title":"APE-GAN: Adversarial perturbation elimination with GAN","volume-title":"Proc. ICLR","author":"Shen"},{"key":"ref12","volume-title":"Mnist Handwritten Digit Database","volume":"2","author":"LeCun","year":"2010"},{"key":"ref13","volume-title":"The Cifar-10 Dataset","author":"Krizhevsky","year":"2014"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.12.038"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2018.00009"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11499"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICME.2019.00095"},{"key":"ref18","article-title":"Quantifying perceptual distortion of adversarial examples","author":"Jordan","year":"2019","journal-title":"arXiv:1902.08265"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11302"},{"key":"ref20","article-title":"Attacking the madry defense model with L1-based adversarial examples","author":"Sharma","year":"2017","journal-title":"arXiv:1710.10733"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00195"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00445"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3287624.3288750"},{"key":"ref24","article-title":"Measuring the transferability of adversarial examples","author":"Petrov","year":"2019","journal-title":"arXiv:1907.06291"},{"key":"ref25","first-page":"1","article-title":"Adversarial examples in the physical world","volume-title":"Proc. ICLR","author":"Kurakin"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2016.36"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.1989.118638"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.07.101"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23198"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134057"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3156\/jsoft.29.5_177_2"},{"key":"ref34","first-page":"1","article-title":"Ensemble adversarial training: Attacks and defenses","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Tram\u00e8r"},{"key":"ref35","first-page":"265","article-title":"TensorFlow: A system for large-scale machine learning","volume-title":"Proc. OSDI","volume":"16","author":"Abadi"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref38","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Kingma"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2017.10.024"},{"key":"ref41","first-page":"5231","article-title":"Imperceptible, robust, and targeted adversarial examples for automatic speech recognition","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Qin"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2925452"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66399-9_4"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107332"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10380310\/09284431.pdf?arnumber=9284431","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:56:28Z","timestamp":1705539388000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9284431\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":44,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3042839","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}