Deep learning captcha recognition for mobile based on TensorFlow

Xiangfeng Lin, Linfu Li, Yu Ren
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Abstract

As the most common captcha, text captcha can prevent others from maliciously using computer programs to log in or attack, and is an important safeguard in Internet authentication. In recent years, with the development of the Internet, the field of artificial intelligence has also developed at a high speed, and convolutional neural networks are widely used in various fields. In this context, for the common problem of character-based captcha recognition, this paper investigates captcha recognition based on a deep learning neural network framework used by the TensorFlow framework with modifications based on the VGG16 convolutional neural network. The 4-digit captcha randomly composed of 64 characters is then converted into an image, and after operations such as image processing and encoding of the captcha, a large number of training sets are generated and the recognition of the captcha is done by the convolutional neural network. Finally, the design GUI interface is deployed to mobile devices with a final accuracy rate of 85% on the test set.
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基于TensorFlow的移动端深度学习验证码识别
文本验证码作为最常见的验证码,可以防止他人恶意利用计算机程序登录或进行攻击,是互联网认证中的重要保障措施。近年来,随着互联网的发展,人工智能领域也得到了高速发展,卷积神经网络在各个领域得到了广泛的应用。在此背景下,针对基于字符的captcha识别的常见问题,本文研究了基于TensorFlow框架的深度学习神经网络框架,并在VGG16卷积神经网络的基础上进行了修改。然后将64个字符随机组成的4位验证码转换成图像,对验证码进行图像处理、编码等操作,生成大量训练集,并由卷积神经网络对验证码进行识别。最后将设计的GUI界面部署到移动设备上,最终在测试集上的准确率达到85%。
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