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