Image-based CAPTCHAs based on neural style transfer
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TLDR
The results prove deep learning can have a positive effect on enhancing CAPTCHA security and provides a promising direction for future CAPTCHAs study.Abstract:
Over the last few years, completely automated public turing test to tell computers and humans apart (CAPTCHA) has been used as an effective method to prevent websites from malicious attacks, however, CAPTCHA designers failed to reach a balance between good usability and high security. In this study, the authors apply neural style transfer to enhance the security for CAPTCHA design. Two image-based CAPTCHAs, Grid-CAPTCHA and Font-CAPTCHA, based on neural style transfer are proposed. Grid-CAPTCHA offers nine stylized images to users and requires users to select all corresponding images according to a short description, and Font-CAPTCHA asks users to click Chinese characters presented in the image in sequence according to the description. To evaluate the effectiveness of this techniques on enhancing CAPTCHA security, they conducted a comprehensive field study and compared them to similar mechanisms. The comparison results demonstrated that the neural style transfer decreased the success rate of automated attacks. Human beings have achieved a successful solving rate of 75.04 and 84.49% on the Grid-CAPTCHA and Font-CAPTCHA schemes, respectively, indicating good usability. The results prove deep learning can have a positive effect on enhancing CAPTCHA security and provides a promising direction for future CAPTCHA study.read more
Citations
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Journal ArticleDOI
Neural Style Transfer and Geometric Transformations for Data Augmentation on Balinese Carving Recognition using MobileNet
TL;DR: A Neural Style Geometric Transformation (NSGT) is proposed as a data augmentation technique for Balinese carvings recognition by combining Neural Style Transfers and Geometric Transformations for a small dataset solution.
Journal ArticleDOI
TICS: text–image-based semantic CAPTCHA synthesis via multi-condition adversarial learning
Xinkang Jia,Jun Xiao,Chao Wu +2 more
TL;DR: This method synthesizes three features: sentence, object, and location to generate a multi-conditional CAPTCHA that can resist the attack of the classification of CNN.
Journal ArticleDOI
CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks
TL;DR: This work proposes a method for generating a CAPTcha image that will resist recognition by machines while maintaining its recognizability to humans, 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.
Journal ArticleDOI
Two-stage human verification using HandCAPTCHA and anti-spoofed finger biometrics with feature selection
TL;DR: In this paper, a human verification scheme was presented to overcome the vulnerabilities of attacks and to enhance security, where a hand image-based CAPTCHA (HandCAPTCHA) was tested to avert automated bot-attacks on the subsequent biometric stage.
Proceedings ArticleDOI
StyleCAPTCHA: CAPTCHA Based on Stylized Images to Defend against Deep Networks
Haitian Chen,Bai Jiang,Hao Chen +2 more
TL;DR: This work proposes a novel CAPTCHA that asks a user to classify stylized human versus animal face images, and proposes Classifier Cross-task Transferability to measure the transferability of a classifier from its original task to another task.
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