.png){kind=icon}
返回
Augment CAPTCHA Security Using Adversarial Examples With Neural Style Transfer
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3298442.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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, 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.
Keyword:
Machine learning
CNN
DNN
CAPTCHA
security
adversarial examples
cognitive
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Gradient-based learning applied to document recognition基于梯度的学习在文档识别中的应用.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏
分享收藏 没有更多内容
没有更多内容