Augment CAPTCHA Security Using Adversarial Examples with Neural Style Transfer

To counteract rising bots, many CAPTCHAs (Completely Automated Public Turing tests to tell Computers and Humans Apart) have been developed throughout the years. Automated attacks [1], however, employing powerful deep learning techniques, have had high success rates over common CAPTCHAs, including im...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Dinh, Nghia, Tran-Trung, Kiet, Hoang, Vinh Truong
Format: Article
Language:English
Subjects:
adversarial examples
Adversarial machine learning
Artificial neural networks
CAPTCHA
CAPTCHAs
CNN
cognitive
Computation
Deep learning
DNN
Image recognition
Logic
Machine learning
Perturbation methods
Resilience
Security
Training
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Summary:To counteract rising bots, many CAPTCHAs (Completely Automated Public Turing tests to tell Computers and Humans Apart) have been developed throughout the years. Automated attacks [1], however, employing powerful deep learning techniques, have had high success rates over common CAPTCHAs, including image-based and text-based CAPTCHAs. Optimistically, introducing imperceptible noise, Adversarial Examples have lately been shown to particularly impact DNN (Deep Neural Network) networks. The authors improved the CAPTCHA security architecture by increasing the resilience of Adversarial Examples when combined with Neural Style Transfer. The findings demonstrated that the proposed approach considerably improves the security of ordinary CAPTCHAs.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3298442