AT-I-FGSM: A novel adversarial CAPTCHA generation method based on gradient adaptive truncation
Junwei Tang, Sijie Zhou, Ping Zhu, Tao Peng, Ruhan He, Xinrong Hu, Changzheng Liu · 2024
Text-based CAPTCHA is widely used in fields such as user identity verification during human-computer interaction in real scenarios. With the development of artificial intelligence, several technologies that automatically bypass CAPTCHAs have emerged, weakening the robustness of CAPTCHAs. In-depth study of adversarial sample technology is needed to further reduce the accuracy of automatic verification code recognition of deep learning models while retaining correct human recognition. We propose a novel method based on gradient adaptive truncation to generate adversarial text-based CAPTCHAs more efficiently. Based on the generated model, our method dynamically adjusts the gradient truncation threshold according to the progress of the perturbation attack method, thereby improving the performance of the sample generation model. On the authoritative dataset, our method is compared with the existing state-of-the-art methods. The results show that our AT-I-FGSM can more effectively reduce the accuracy of automatic recognition models to identify CAPTCHAs and improve the security of CAPTCHAs. At the same time, our method consumes less time in generating CAPTCHAs.