Research And Implementation Of Text CAPTCHA Defense Enhancing Scheme Based On Adversarial Example

Text CAPTCHA is widely used in the Internet as a security measure to protect the website from automatic crawler or malicious traffic attack.In recent years,with the rapid development of neural network,the application of convolutional neural network in the non-segmentation recognition of text verification code has become a hot topic.This makes the performance of text captcha defense more challenging.How to protect text CAPTCHA in the context of recognition based on convolutional neural network has become an urgent problem.As a subtle disturbance carefully constructed,the adversarial noise can be added to the original sample to lead the neural network to misclassify the previously correctly classified samples.In order to improve the defense ability of captcha,this paper applies the adversarial examples to text CAPTCHA.However,if the adversarial example generation algorithm is simply applied to the text captcha,the adversarial text CAPTCHA not only has poor generalization ability,but also has insufficient anti-preprocessing ability.So this paper designs an iterative adversarial generative framework,convolution neural networks combined with pretreatment module to form a discriminant module,the weight saliency map are used in generating module to filter which feature point should be modified.By the game of generative module and discriminative modules within the framework,the defense capability of verification code can be enhanced.The adversarial text CAPTCHA generated by the framework have stronger ability of generalization,which can widely reduce the variety of mainstream convolution neural network recognition ability.At the same time,adversarial text CAPTCHA is able to resist pretreatment without significantly reducing the user experience,which keeps human users availability with improving CAPTCHA for a variety of mainstream convolution neural network automatically recognition defenses.The experimental results show that text CAPTCHA can be greatly enhanced by the framework against automatic recognition accuracy based on convolutional neural network.