{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T16:03:58Z","timestamp":1782576238533,"version":"3.54.5"},"reference-count":47,"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\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2020M680563"],"award-info":[{"award-number":["2020M680563"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Program of China","award":["2018AAA0100701"],"award-info":[{"award-number":["2018AAA0100701"]}]},{"DOI":"10.13039\/501100014219","name":"National Science Fund for Distinguished Young Scholars","doi-asserted-by":"publisher","award":["51625503"],"award-info":[{"award-number":["51625503"]}],"id":[{"id":"10.13039\/501100014219","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Signal Process. Lett."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/lsp.2021.3090944","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T19:52:16Z","timestamp":1624305136000},"page":"1340-1344","source":"Crossref","is-referenced-by-count":5,"title":["DiFNet: Densely High-Frequency Convolutional Neural Networks"],"prefix":"10.1109","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1115-8175","authenticated-orcid":false,"given":"Wenzheng","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4363-6108","authenticated-orcid":false,"given":"Jianqiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8088-367X","authenticated-orcid":false,"given":"Changshui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s11571-011-9183-8"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-96868-6_57"},{"key":"ref33","article-title":"Ensemble methods as a defense to adversarial perturbations against deep neural networks","author":"strauss","year":"2017"},{"key":"ref32","first-page":"550","article-title":"Residual networks behave like ensembles of relatively shallow networks","author":"veit","year":"2016","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00059"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICSESS.2017.8342913"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICMA.2014.6885761"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1986.4767851"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICNC47757.2020.9049702"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/srep07861"},{"key":"ref40","article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"CoRR"},{"key":"ref11","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"0","journal-title":"Proc 3rd Int Conf Learn Represent"},{"key":"ref12","article-title":"Towards deep learning models resistant to adversarial attacks","author":"madry","year":"0","journal-title":"Proc 6th Int Conf Learn Represent"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref14","article-title":"Ead: Elastic-net attacks to deep neural networks via adversarial examples","author":"chen","year":"2017"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.282"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00957"},{"key":"ref17","first-page":"3866","article-title":"NATTACK: Learning the distributions of adversarial examples for an improved black-box attack on deep neural networks","author":"li","year":"0","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.172"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2019.2890858"},{"key":"ref28","article-title":"Defense-GAN: Protecting classifiers against adversarial attacks using generative models","author":"samangouei","year":"0","journal-title":"Proc 6th Int Conf Learn Represent"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"ref27","article-title":"Thermometer encoding: One hot way to resist adversarial examples","author":"buckman","year":"0","journal-title":"Proc 6th Int Conf Learn Represent"},{"key":"ref3","article-title":"Delving into transferable adversarial examples and black-box attacks","author":"liu","year":"0","journal-title":"Proc 5th Int Conf Learn Represent"},{"key":"ref6","article-title":"Adversarial machine learning at scale","author":"kurakin","year":"0","journal-title":"Proc 5th Int Conf Learn Represent"},{"key":"ref29","article-title":"Countering adversarial images using input transformations","author":"guo","year":"0","journal-title":"Proc 6th Int Conf Learn Represent"},{"key":"ref5","first-page":"506","article-title":"Ensemble adversarial training: Attacks and defenses","author":"tram\u00e8rs","year":"0","journal-title":"Proc 6th Int Conf Learn Represent"},{"key":"ref8","article-title":"Are odds really odd? bypassing statistical detection of adversarial examples","author":"hosseini","year":"2019"},{"key":"ref7","article-title":"The odds are odd: A statistical test for detecting adversarial examples","author":"roth","year":"2019"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978392"},{"key":"ref9","first-page":"274","article-title":"Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples","author":"athalye","year":"0","journal-title":"Proc 35th Int Conf Mach Learn"},{"key":"ref1","article-title":"Adversarial examples in the physical world","author":"kurakin","year":"2016","journal-title":"CoRR"},{"key":"ref46","article-title":"Adversarial examples in the physical world","author":"kurakin","year":"0","journal-title":"Proc 5th Int Conf Learn Represent"},{"key":"ref20","first-page":"7472","article-title":"Theoretically principled trade-off between robustness and accuracy","author":"zhang","year":"0","journal-title":"Proc 36th Int Conf Mach Learn"},{"key":"ref45","first-page":"7472","article-title":"Theoretically principled trade-off between robustness and accuracy","author":"zhang","year":"0"},{"key":"ref22","first-page":"3353","article-title":"Adversarial training for free","author":"shafahi","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref47","article-title":"Simple black-box adversarial perturbations for deep networks","author":"narodytska","year":"2016"},{"key":"ref21","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"0","journal-title":"Proc 3rd Int Conf Learn Represent"},{"key":"ref42","article-title":"AdverTorch v0.1: An adversarial robustness toolbox based on pytorch","author":"ding","year":"2019"},{"key":"ref24","article-title":"On detecting adversarial perturbations","author":"metzen","year":"0","journal-title":"Proc 5th Int Conf Learn Represent"},{"key":"ref41","doi-asserted-by":"crossref","first-page":"p. 2607","DOI":"10.21105\/joss.02607","article-title":"Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax","volume":"5","author":"rauber","year":"2020","journal-title":"Open Source Software"},{"key":"ref23","article-title":"Fast is better than free: Revisiting adversarial training","author":"wong","year":"0","journal-title":"Proc 8th Int Conf Learn Represent"},{"key":"ref44","article-title":"Ensemble adversarial training: Attacks and defenses","author":"tram\u00e8r","year":"0","journal-title":"Proc 6th Int Conf Learn Represent"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00191"},{"key":"ref43","article-title":"Torchattacks: A pytorch repository for adversarial attacks","author":"kim","year":"2020"},{"key":"ref25","article-title":"Towards deep neural network architectures robust to adversarial examples","author":"gu","year":"0","journal-title":"Proc 3rd Int Conf Learn Represent"}],"container-title":["IEEE Signal Processing Letters"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/97\/9325893\/09461402.pdf?arnumber=9461402","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:50:36Z","timestamp":1652194236000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9461402\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":47,"URL":"https:\/\/doi.org\/10.1109\/lsp.2021.3090944","relation":{},"ISSN":["1070-9908","1558-2361"],"issn-type":[{"value":"1070-9908","type":"print"},{"value":"1558-2361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}