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dc.contributor.authorSun, Fengen_US
dc.contributor.authorZhang, Zhenjiangen_US
dc.contributor.authorKao, Yi-Chihen_US
dc.contributor.authorLi, Tianzhouen_US
dc.contributor.authorShen, Boen_US
dc.date.accessioned2019-10-05T00:08:44Z-
dc.date.available2019-10-05T00:08:44Z-
dc.date.issued2019-01-01en_US
dc.identifier.issn1607-9264en_US
dc.identifier.urihttp://dx.doi.org/10.3966/160792642019072004028en_US
dc.identifier.urihttp://hdl.handle.net/11536/152843-
dc.description.abstractNowadays, with the development of artificial intelligence, deep learning has attracted more and more attention. Whereas deep neural network has made incredible progress in many domains including Computer Vision, Nature Language Processing, etc, recent studies show that they are vulnerable to the adversarial attacks which takes legitimate images with undetected perturbation as input and can mislead the model to predict incorrect outputs. We consider that the key point of the adversarial attack is the undetected perturbation added to the input. It will be of great significance to eliminate the effect of the added noise. Thus, we design a new, efficient model based on residual image which can detect this potential adversarial attack. We design a method to get the residual image which can capture these possible perturbations. Based on the residual image we got, the detection mechanism can help us detect whether it is an adversarial image or not. A serial of experiments has also been carried out. Subsequent experiments prove that the new detection method can detect the adversarial attack with high effectivity.en_US
dc.language.isoen_USen_US
dc.subjectAdversarial attacken_US
dc.subjectDetection mechanismen_US
dc.subjectResidual imageen_US
dc.titleA New Method to Detect the Adversarial Attack Based on the Residual Imageen_US
dc.typeArticleen_US
dc.identifier.doi10.3966/160792642019072004028en_US
dc.identifier.journalJOURNAL OF INTERNET TECHNOLOGYen_US
dc.citation.volume20en_US
dc.citation.issue4en_US
dc.citation.spage1297en_US
dc.citation.epage1304en_US
dc.contributor.department資訊技術服務中心zh_TW
dc.contributor.departmentInformation Technology Services Centeren_US
dc.identifier.wosnumberWOS:000483464100029en_US
dc.citation.woscount0en_US
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