{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T15:34:10Z","timestamp":1771515250514,"version":"3.50.1"},"reference-count":53,"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:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972306"],"award-info":[{"award-number":["61972306"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Laboratory","award":["2021KD0AB03"],"award-info":[{"award-number":["2021KD0AB03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tip.2021.3092582","type":"journal-article","created":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T19:54:35Z","timestamp":1625169275000},"page":"6117-6129","source":"Crossref","is-referenced-by-count":58,"title":["Defense Against Adversarial Attacks by Reconstructing Images"],"prefix":"10.1109","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7358-5543","authenticated-orcid":false,"given":"Shudong","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4969-5718","authenticated-orcid":false,"given":"Haichang","family":"Gao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7676-4494","authenticated-orcid":false,"given":"Qingxun","family":"Rao","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","article-title":"Feature squeezing: Detecting adversarial examples in deep neural networks","author":"xu","year":"2017","journal-title":"arXiv 1704 01155"},{"key":"ref38","first-page":"1831","article-title":"Defense against adversarial attacks using feature scattering-based adversarial training","author":"zhang","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref33","article-title":"Improving transferability of adversarial examples with input diversity","author":"xie","year":"2018","journal-title":"arXiv 1803 06978"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref31","article-title":"Towards deep learning models resistant to adversarial attacks","author":"madry","year":"2017","journal-title":"arXiv 1706 06083"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00348"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00059"},{"key":"ref35","article-title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","author":"croce","year":"2020","journal-title":"arXiv 2003 01690"},{"key":"ref34","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","author":"athalye","year":"2018","journal-title":"arXiv 1802 00420"},{"key":"ref28","article-title":"Image super-resolution as a defense against adversarial attacks","author":"mustafa","year":"2019","journal-title":"arXiv 1901 01677"},{"key":"ref27","article-title":"Defense-GAN: Protecting classifiers against adversarial attacks using generative models","author":"samangouei","year":"2018","journal-title":"arXiv 1805 06605"},{"key":"ref29","first-page":"694","article-title":"Perceptual losses for real-time style transfer and super-resolution","author":"johnson","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref2","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref20","first-page":"2574","article-title":"Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients","author":"ross","year":"2018","journal-title":"Proc 32nd AAAI Conf Artif Intell"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.41"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00191"},{"key":"ref23","article-title":"A study of the effect of JPG compression on adversarial images","author":"dziugaite","year":"2016","journal-title":"arXiv 1608 00853"},{"key":"ref26","article-title":"PixelDefend: Leveraging generative models to understand and defend against adversarial examples","author":"song","year":"2017","journal-title":"arXiv 1710 10766"},{"key":"ref25","article-title":"Mitigating adversarial effects through randomization","author":"xie","year":"2017","journal-title":"arXiv 1711 01991"},{"key":"ref50","article-title":"Technical report on the CleverHans v2.1.0 adversarial examples library","author":"papernot","year":"2016","journal-title":"arXiv 1610 00768"},{"key":"ref51","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref53","article-title":"Robustness may be at odds with accuracy","author":"tsipras","year":"2018","journal-title":"arXiv 1805 12152"},{"key":"ref52","article-title":"On adaptive attacks to adversarial example defenses","author":"tramer","year":"2020","journal-title":"arXiv 2002 08347"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00894"},{"key":"ref12","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"2014","journal-title":"arXiv 1412 6572"},{"key":"ref13","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"2013","journal-title":"arXiv 1312 6199"},{"key":"ref14","article-title":"Adversarial examples in the physical world","author":"kurakin","year":"2016","journal-title":"arXiv 1607 02533"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref17","article-title":"Countering adversarial images using input transformations","author":"guo","year":"2017","journal-title":"arXiv 1711 00117"},{"key":"ref18","article-title":"Adversarial machine learning at scale","author":"kurakin","year":"2016","journal-title":"arXiv 1611 01236"},{"key":"ref19","article-title":"Ensemble adversarial training: Attacks and defenses","author":"tram\u00e8r","year":"2017","journal-title":"arXiv 1705 07204"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref3","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2959256"},{"key":"ref5","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00061"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2898567"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2019.2936503"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref48","first-page":"1","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"szegedy","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134057"},{"key":"ref41","article-title":"ComDefend: An efficient image compression model to defend adversarial examples","author":"jia","year":"2018","journal-title":"arXiv 1811 12673"},{"key":"ref44","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"arXiv 1502 03167"},{"key":"ref43","author":"gross","year":"2016","journal-title":"Training and investigating residual nets"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9263394\/09470919.pdf?arnumber=9470919","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:50:14Z","timestamp":1652194214000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9470919\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/tip.2021.3092582","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}