Department of Electrical Engineering, Korea Military Academy, Seoul 01805, Korea.
School of Computing, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea.
Sensors (Basel). 2020 Mar 9;20(5):1495. doi: 10.3390/s20051495.
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.
移动设备（如传感器）用于连接互联网并向用户提供服务。Web 服务容易受到自动化攻击，这可能会限制移动设备访问网站。为了防止这种自动化攻击，验证码（CAPTCHA）被广泛用作安全解决方案。然而，当验证码受到高度扭曲以使其能够抵御自动化攻击时，人类将难以识别。在这项工作中，我们提出了一种生成验证码图像的方法，该方法既可以抵抗机器的识别，又可以保持其对人类的可识别性。该方法利用风格迁移方法，通过将其他图像的风格融入到原始 CAPTCHA 的内容中，创建一个新的图像，称为风格插入的 CAPTCHA 图像。在我们的实验中，我们使用了 TensorFlow 机器学习库和六个实际网站上使用的 CAPTCHA 数据集。实验结果表明，该方案使用一张风格图像和两张风格图像分别将 DeCAPTCHA 系统的识别率降低到 3.5%和 3.2%，同时保持了人类的可识别性。
