CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks
Mobile devices such as sensors are used to connect to the Internet and provide services to users. Web services are vulnerable to automated attacks, which can restrict mobile devices from accessing websites. To prevent such automated attacks, CAPTCHAs are widely used as a security solution. However,...
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Format: | Article |
Language: | English |
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MDPI AG
2020-03-01
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Series: | Sensors |
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Online Access: | https://www.mdpi.com/1424-8220/20/5/1495 |
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author | Hyun Kwon Hyunsoo Yoon Ki-Woong Park |
author_facet | Hyun Kwon Hyunsoo Yoon Ki-Woong Park |
author_sort | Hyun Kwon |
collection | DOAJ |
description | Mobile devices such as sensors are used to connect to the Internet and provide services to users. Web services are vulnerable to automated attacks, which can restrict mobile devices from accessing websites. To prevent such automated attacks, CAPTCHAs are widely used as a security solution. However, when a high level of distortion has been applied to a CAPTCHA to make it resistant to automated attacks, the CAPTCHA becomes difficult for a human to recognize. In this work, we propose a method for generating a CAPTCHA image that will resist recognition by machines while maintaining its recognizability to humans. The method utilizes the style transfer method, and creates a new image, called a style-plugged-CAPTCHA image, by incorporating the styles of other images while keeping the content of the original CAPTCHA. In our experiment, we used the TensorFlow machine learning library and six CAPTCHA datasets in use on actual websites. The experimental results show that the proposed scheme reduces the rate of recognition by the DeCAPTCHA system to 3.5% and 3.2% using one style image and two style images, respectively, while maintaining recognizability by humans. |
first_indexed | 2024-04-11T12:40:37Z |
format | Article |
id | doaj.art-15f684b3634c44e1a8f1cc45f072f8b9 |
institution | Directory Open Access Journal |
issn | 1424-8220 |
language | English |
last_indexed | 2024-04-11T12:40:37Z |
publishDate | 2020-03-01 |
publisher | MDPI AG |
record_format | Article |
series | Sensors |
spelling | doaj.art-15f684b3634c44e1a8f1cc45f072f8b92022-12-22T04:23:29ZengMDPI AGSensors1424-82202020-03-01205149510.3390/s20051495s20051495CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural NetworksHyun Kwon0Hyunsoo Yoon1Ki-Woong Park2Department of Electrical Engineering, Korea Military Academy, Seoul 01805, KoreaSchool of Computing, Korea Advanced Institute of Science and Technology, Daejeon 34141, KoreaDepartment of Computer and Information Security, Sejong University, Seoul 05006, KoreaMobile devices such as sensors are used to connect to the Internet and provide services to users. Web services are vulnerable to automated attacks, which can restrict mobile devices from accessing websites. To prevent such automated attacks, CAPTCHAs are widely used as a security solution. However, when a high level of distortion has been applied to a CAPTCHA to make it resistant to automated attacks, the CAPTCHA becomes difficult for a human to recognize. In this work, we propose a method for generating a CAPTCHA image that will resist recognition by machines while maintaining its recognizability to humans. The method utilizes the style transfer method, and creates a new image, called a style-plugged-CAPTCHA image, by incorporating the styles of other images while keeping the content of the original CAPTCHA. In our experiment, we used the TensorFlow machine learning library and six CAPTCHA datasets in use on actual websites. The experimental results show that the proposed scheme reduces the rate of recognition by the DeCAPTCHA system to 3.5% and 3.2% using one style image and two style images, respectively, while maintaining recognizability by humans.https://www.mdpi.com/1424-8220/20/5/1495machine learningneural networkimage style transfercaptchaconvolutional neural network (cnn) |
spellingShingle | Hyun Kwon Hyunsoo Yoon Ki-Woong Park CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks Sensors machine learning neural network image style transfer captcha convolutional neural network (cnn) |
title | CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks |
title_full | CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks |
title_fullStr | CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks |
title_full_unstemmed | CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks |
title_short | CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks |
title_sort | captcha image generation two step style transfer learning in deep neural networks |
topic | machine learning neural network image style transfer captcha convolutional neural network (cnn) |
url | https://www.mdpi.com/1424-8220/20/5/1495 |
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