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dc.contributor.authorChe, Aolin
dc.contributor.authorLiu, Yalin
dc.contributor.authorXiao, Hong
dc.contributor.authorWang, Hao
dc.contributor.authorZhang, Ke
dc.contributor.authorDai, Hong-Ning
dc.identifier.citationSecurity and Communication Networks. 2021, .en_US
dc.description.abstractIn the past decades, due to the low design cost and easy maintenance, text-based CAPTCHAs have been extensively used in constructing security mechanisms for user authentications. With the recent advances in machine/deep learning in recognizing CAPTCHA images, growing attack methods are presented to break text-based CAPTCHAs. These machine learning/deep learning-based attacks often rely on training models on massive volumes of training data. The poorly constructed CAPTCHA data also leads to low accuracy of attacks. To investigate this issue, we propose a simple, generic, and effective preprocessing approach to filter and enhance the original CAPTCHA data set so as to improve the accuracy of the previous attack methods. In particular, the proposed preprocessing approach consists of a data selector and a data augmentor. The data selector can automatically filter out a training data set with training significance. Meanwhile, the data augmentor uses four different image noises to generate different CAPTCHA images. The well-constructed CAPTCHA data set can better train deep learning models to further improve the accuracy rate. Extensive experiments demonstrate that the accuracy rates of five commonly used attack methods after combining our preprocessing approach are 2.62% to 8.31% higher than those without preprocessing approach. Moreover, we also discuss potential research directions for future work.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleAugmented Data Selector to Initiate Text-Based CAPTCHA Attacken_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.source.journalSecurity and Communication Networksen_US
dc.description.localcodeCopyright © 2021 Aolin Che et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US

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Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal