A Semi-supervised Deep Learning-Based Solver for Breaking Text-Based CAPTCHAs
Xianwen Deng, Ruijie Zhao, Zhi Xue, Ming Liu, Libo Chen, Yijun Wang · 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · 2021
Text-based CAPTCHAs are still the most widely used CAPTCHA mode. Many researchers have proposed attack methods to break them. In previous attacks, segmentation-based methods require at least three steps: preprocessing, segmentation, and recognition, which means that different modes of CAPTCHA require various preprocessing and segmentation algorithms. In recent years, a series of deep learning (DL) models have been designed for cracking text-based CAPTCHAs. However, these methods require annotating numerous images, which are time-consuming and labor-intensive. In this paper, we propose a semi-supervised DL-based solver for breaking text-based CAPTCHAs, which can use a small number of labeled CAPTCHAs to achieve a high-performance attack model. The CNN module and the attention-based Seq2Seq module are two key components for effective feature extraction and character recognition. The experimental results show that our solver successfully attacked 9 types of most popular text-based CAPTCHAs, and the attack success rate is better than the four latest attack models. In addition, our model does not perform any data preprocessing and has a fast attack speed, making it more suitable for real-time attacks. The code and dataset are available on the github.