Research Article
Meta Perturbation Generation Network for Text-Based CAPTCHA
@INPROCEEDINGS{10.1007/978-3-031-64948-6_6, author={Zhuoting Wu and Zhiwei Guo and Jiuxiang You and Zhenguo Yang and Qing Li and Wenyin Liu}, title={Meta Perturbation Generation Network for Text-Based CAPTCHA}, proceedings={Security and Privacy in Communication Networks. 19th EAI International Conference, SecureComm 2023, Hong Kong, China, October 19-21, 2023, Proceedings, Part I}, proceedings_a={SECURECOMM}, year={2024}, month={10}, keywords={Text-based CAPTCHA Meta-learning Adversarial perturbation CAPTCHA images generation}, doi={10.1007/978-3-031-64948-6_6} }- Zhuoting Wu
Zhiwei Guo
Jiuxiang You
Zhenguo Yang
Qing Li
Wenyin Liu
Year: 2024
Meta Perturbation Generation Network for Text-Based CAPTCHA
SECURECOMM
Springer
DOI: 10.1007/978-3-031-64948-6_6
Abstract
With the development of text-based CAPTCHA, many adversarial example generation methods for text-based CAPTCHA have been proposed. However, the perturbation factors generated by the existing methods are simple and easy to be attacked. In this paper, we present a framework for meta perturbation text-based CAPTCHA generation (denoted as MAPFN), which enhances the security of text-based CAPTCHA and makes the perturbed images friendly for humans. More specifically, we propose a meta perturbation generation network (MPGN) to construct rich and effective perturbation factors. To this end, we devise a perturbation feature fusion module (PFFM) to fuse the perturbation factors generated by MPGN into a new perturbation factor, which can be applied to the CAPTCHA image to make it similar to the origin while being effectively against the attacker models. Extensive experiments on 8 real website CAPTCHA datasets show the excellent performance of the proposed MAPFN. (e.g., attack accuracy falls from 93.99% to 0.98% on the NSFC dataset).
