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Your search keyword '"Wei, Yuliang"' showing total 38 results
Start Over Author "Wei, Yuliang" Publisher elsevier bv
38 results on '"Wei, Yuliang"'

1. The Interaction between Aspergillus Brasiliensis and Exposed Copper Circuits in the Space Microgravity Environment

Author

Liu, Qianqian, primary, Zhang, Qin, additional, Dang, Lei, additional, Chen, Nana, additional, Yin, Zhao, additional, Ma, Lingling, additional, Feng, Yali, additional, Li, Weiliang, additional, Wei, Yuliang, additional, Zhang, Wende, additional, Lu, Lin, additional, Dong, Chaofang, additional, Yuan, Junxia, additional, and Xiao, Kui, additional

Published
2024
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12. Make complex CAPTCHAs simple: A fast text captcha solver based on a small number of samples

Author

Yang Liu, Bailing Wang, Wei Yuliang, Wang Yao, and Mingjin Zhang

Subjects
Information Systems and Management, CAPTCHA, business.product_category, business.industry, Computer science, Small number, ComputingMilieux_PERSONALCOMPUTING, Process (computing), Solver, computer.software_genre, Machine learning, Computer Science Applications, Theoretical Computer Science, Image (mathematics), ComputingMilieux_MANAGEMENTOFCOMPUTINGANDINFORMATIONSYSTEMS, Transformation (function), Artificial Intelligence, Control and Systems Engineering, Laptop, Segmentation, Artificial intelligence, business, computer, Software
Abstract

Text-based captchas are still widely used by many websites such as Wikipedia and Microsoft despite the emergence of many alternative captchas. Recently, the design of text-based captchas has become more and more complex to resist attacks from automatic cracking programs. However, most of the existing captcha solving methods have certain shortcomings, such as insufficient accuracy, poor generalization performance, and the need for a large number of labeled samples. This study proposes a fast captcha solver that can effectively break text-based captchas with complex security features using a small amount of labeled data. The solver was achieved by constructing a captcha transformation model based on generative adversarial networks to simplify the captcha images before character segmentation and recognition. Results showed that the proposed captcha solver achieved a high success rate of over 96% character accuracy and 74% captcha accuracy for all evaluated schemes. Moreover, the average time to process a single captcha image using a laptop GPU was only 4–8 ms. The effectiveness of this work may encourage captcha designers to reconsider a more secure human–machine distinction mechanism.

Published
2021

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