{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T12:20:15Z","timestamp":1769516415521,"version":"3.49.0"},"reference-count":5,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T00:00:00Z","timestamp":1582243200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Plan of China","award":["2017YFB1002502"],"award-info":[{"award-number":["2017YFB1002502"]}]},{"name":"National Key Research and Development Plan of China","award":["61701089"],"award-info":[{"award-number":["61701089"]}]},{"name":"National Key Research and Development Plan of China","award":["162300410333"],"award-info":[{"award-number":["162300410333"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2017YFB1002502"],"award-info":[{"award-number":["2017YFB1002502"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61701089"],"award-info":[{"award-number":["61701089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["162300410333"],"award-info":[{"award-number":["162300410333"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["2017YFB1002502"],"award-info":[{"award-number":["2017YFB1002502"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["61701089"],"award-info":[{"award-number":["61701089"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["162300410333"],"award-info":[{"award-number":["162300410333"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2020,2,21]]},"abstract":"<jats:p>In image classification of deep learning, adversarial examples where input is intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect results, which means DNNs are vulnerable to them. Different attack and defense strategies have been proposed to better research the mechanism of deep learning. However, those researches in these networks are only for one aspect, either an attack or a defense. There is in the improvement of offensive and defensive performance, and it is difficult to promote each other in the same framework. In this paper, we propose Cycle-Consistent Adversarial GAN (CycleAdvGAN) to generate adversarial examples, which can learn and approximate the distribution of the original instances and adversarial examples, especially promoting attackers and defenders to confront each other and improve their ability. For CycleAdvGAN, once the Generator<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mi>A<\/mml:mi><\/mml:math> and <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mi>D<\/mml:mi><\/mml:math> are trained, <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mrow><mml:msub><mml:mrow><mml:mi>G<\/mml:mi><\/mml:mrow><mml:mrow><mml:mi>A<\/mml:mi><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math> can generate adversarial perturbations ef\ufb01ciently for any instance, improving the performance of the existing attack methods, and <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mrow><mml:msub><mml:mrow><mml:mi>G<\/mml:mi><\/mml:mrow><mml:mrow><mml:mi>D<\/mml:mi><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math> can generate recovery adversarial examples to clean instances, defending against existing attack methods. We apply CycleAdvGAN under semiwhite-box and black-box settings on two public datasets MNIST and CIFAR10. Using the extensive experiments, we show that our method has achieved the state-of-the-art adversarial attack method and also has efficiently improved the defense ability, which made the integration of adversarial attack and defense come true. In addition, it has improved the attack effect only trained on the adversarial dataset generated by any kind of adversarial attack.<\/jats:p>","DOI":"10.1155\/2020\/3608173","type":"journal-article","created":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T18:34:54Z","timestamp":1582310094000},"page":"1-9","source":"Crossref","is-referenced-by-count":11,"title":["Cycle-Consistent Adversarial GAN: The Integration of Adversarial Attack and Defense"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5967-3021","authenticated-orcid":true,"given":"Lingyun","family":"Jiang","sequence":"first","affiliation":[{"name":"Academy of Information Systems Engineering, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7057-9562","authenticated-orcid":true,"given":"Kai","family":"Qiao","sequence":"additional","affiliation":[{"name":"Academy of Information Systems Engineering, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0836-8179","authenticated-orcid":true,"given":"Ruoxi","family":"Qin","sequence":"additional","affiliation":[{"name":"Academy of Information Systems Engineering, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1087-5911","authenticated-orcid":true,"given":"Linyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Academy of Information Systems Engineering, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanting","family":"Yu","sequence":"additional","affiliation":[{"name":"Academy of Information Systems Engineering, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2135-8752","authenticated-orcid":true,"given":"Jian","family":"Chen","sequence":"additional","affiliation":[{"name":"Academy of Information Systems Engineering, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haibing","family":"Bu","sequence":"additional","affiliation":[{"name":"Academy of Information Systems Engineering, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8268-559X","authenticated-orcid":true,"given":"Bin","family":"Yan","sequence":"additional","affiliation":[{"name":"Academy of Information Systems Engineering, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","first-page":"1097","volume-title":"ImageNet classification with deep convolutional neural networks","year":"2012"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/msp.2012.2205597"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"26","year":"2009"},{"issue":"1","key":"30","first-page":"1929","volume":"15","year":"2014","journal-title":"The Journal of Machine Learning Research"}],"container-title":["Security and Communication Networks"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2020\/3608173.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2020\/3608173.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2020\/3608173.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T18:34:59Z","timestamp":1582310099000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/scn\/2020\/3608173\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,21]]},"references-count":5,"alternative-id":["3608173","3608173"],"URL":"https:\/\/doi.org\/10.1155\/2020\/3608173","relation":{},"ISSN":["1939-0114","1939-0122"],"issn-type":[{"value":"1939-0114","type":"print"},{"value":"1939-0122","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,21]]}}}