A Low-Cost Approach to Crack Python CAPTCHAs Using AI-Based Chosen-Plaintext Attack
AbstractCAPTCHA 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. View Full-Text
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Yu, N.; Darling, K. A Low-Cost Approach to Crack Python CAPTCHAs Using AI-Based Chosen-Plaintext Attack. Appl. Sci. 2019, 9, 2010.
Yu N, Darling K. A Low-Cost Approach to Crack Python CAPTCHAs Using AI-Based Chosen-Plaintext Attack. Applied Sciences. 2019; 9(10):2010.Chicago/Turabian Style
Yu, Ning; Darling, Kyle. 2019. "A Low-Cost Approach to Crack Python CAPTCHAs Using AI-Based Chosen-Plaintext Attack." Appl. Sci. 9, no. 10: 2010.
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