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A Low-Cost Approach to Crack Python CAPTCHAs Using AI-Based Chosen-Plaintext Attack

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Τίτλος: A Low-Cost Approach to Crack Python CAPTCHAs Using AI-Based Chosen-Plaintext Attack
Συγγραφείς: Ning Yu, Kyle Darling
Πηγή: Applied Sciences, Vol 9, Iss 10, p 2010 (2019)
Στοιχεία εκδότη: MDPI AG
Έτος έκδοσης: 2019
Συλλογή: Directory of Open Access Journals: DOAJ Articles
Θεματικοί όροι: CAPTCHA security, authentication, open-source Python library, deep learning, convolutional neural network, TensorFlow, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Περιγραφή: CAPTCHA authentication has been challenged by recent technology advances in AI. However, many of the AI advances challenging CAPTCHA are either restricted by a limited amount of labeled CAPTCHA data or are constructed in an expensive or complicated way. In contrast, this paper illustrates a low-cost approach that takes advantage of the nature of open source libraries for an AI-based chosen-plaintext attack. The chosen-plaintext attack described here relies on a deep learning model created and trained on a simple personal computer in a low-cost way. It shows an efficient cracking rate over two open-source Python CAPTCHA Libraries, Claptcha and Captcha. This chosen-plaintext attack method has raised a potential security alert in the era of AI, particularly to small-business owners who use the open-source CAPTCHA libraries. The main contributions of this project include: (1) it is the first low-cost method based on chosen-plaintext attack by using the nature of open-source Python CAPTCHA libraries; (2) it is a novel way to combine TensorFlow object detection and our proposed peak segmentation algorithm with convolutional neural network to improve the recognition accuracy.
Τύπος εγγράφου: article in journal/newspaper
Γλώσσα: English
Relation: https://www.mdpi.com/2076-3417/9/10/2010; https://doaj.org/toc/2076-3417; https://doaj.org/article/046ee2e167c04860a940d113ddffd2b4
DOI: 10.3390/app9102010
Διαθεσιμότητα: https://doi.org/10.3390/app9102010
https://doaj.org/article/046ee2e167c04860a940d113ddffd2b4
Αριθμός Καταχώρησης: edsbas.AD735CAF
Βάση Δεδομένων: BASE
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  Data: A Low-Cost Approach to Crack Python CAPTCHAs Using AI-Based Chosen-Plaintext Attack
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  Data: <searchLink fieldCode="AR" term="%22Ning+Yu%22">Ning Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Kyle+Darling%22">Kyle Darling</searchLink>
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  Data: Applied Sciences, Vol 9, Iss 10, p 2010 (2019)
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  Data: <searchLink fieldCode="DE" term="%22CAPTCHA+security%22">CAPTCHA security</searchLink><br /><searchLink fieldCode="DE" term="%22authentication%22">authentication</searchLink><br /><searchLink fieldCode="DE" term="%22open-source+Python+library%22">open-source Python library</searchLink><br /><searchLink fieldCode="DE" term="%22deep+learning%22">deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22convolutional+neural+network%22">convolutional neural network</searchLink><br /><searchLink fieldCode="DE" term="%22TensorFlow%22">TensorFlow</searchLink><br /><searchLink fieldCode="DE" term="%22Technology%22">Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+%28General%29%2E+Civil+engineering+%28General%29%22">Engineering (General). Civil engineering (General)</searchLink><br /><searchLink fieldCode="DE" term="%22TA1-2040%22">TA1-2040</searchLink><br /><searchLink fieldCode="DE" term="%22Biology+%28General%29%22">Biology (General)</searchLink><br /><searchLink fieldCode="DE" term="%22QH301-705%2E5%22">QH301-705.5</searchLink><br /><searchLink fieldCode="DE" term="%22Physics%22">Physics</searchLink><br /><searchLink fieldCode="DE" term="%22QC1-999%22">QC1-999</searchLink><br /><searchLink fieldCode="DE" term="%22Chemistry%22">Chemistry</searchLink><br /><searchLink fieldCode="DE" term="%22QD1-999%22">QD1-999</searchLink>
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  Data: CAPTCHA authentication has been challenged by recent technology advances in AI. However, many of the AI advances challenging CAPTCHA are either restricted by a limited amount of labeled CAPTCHA data or are constructed in an expensive or complicated way. In contrast, this paper illustrates a low-cost approach that takes advantage of the nature of open source libraries for an AI-based chosen-plaintext attack. The chosen-plaintext attack described here relies on a deep learning model created and trained on a simple personal computer in a low-cost way. It shows an efficient cracking rate over two open-source Python CAPTCHA Libraries, Claptcha and Captcha. This chosen-plaintext attack method has raised a potential security alert in the era of AI, particularly to small-business owners who use the open-source CAPTCHA libraries. The main contributions of this project include: (1) it is the first low-cost method based on chosen-plaintext attack by using the nature of open-source Python CAPTCHA libraries; (2) it is a novel way to combine TensorFlow object detection and our proposed peak segmentation algorithm with convolutional neural network to improve the recognition accuracy.
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      – SubjectFull: authentication
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