With the rapid advancements in deep learning, traditional CAPTCHA schemes are increasingly vulnerable to automated attacks powered by deep neural networks (DNNs). Existing adversarial attack methods often rely on original image characteristics, resulting in distortions that hinder human interpretation and limit applicability in scenarios lacking initial input images. To address these challenges, we propose the Unsourced Adversarial CAPTCHA (UAC), a novel framework generating high-fidelity adversarial examples guided by attacker-specified text prompts. Leveraging a Large Language Model (LLM), UAC enhances CAPTCHA diversity and supports both targeted and untargeted attacks. For targeted attacks, the EDICT method optimizes dual latent variables in a diffusion model for superior image quality. In untargeted attacks, especially for black-box scenarios, we introduce bi-path unsourced adversarial CAPTCHA (BP-UAC), a two-step optimization strategy employing multimodal gradients and bi-path optimization for efficient misclassification. Experiments show BP-UAC achieves high attack success rates across diverse systems, generating natural CAPTCHAs indistinguishable to humans and DNNs.
View on arXiv@article{du2025_2506.10685,
title={ Defensive Adversarial CAPTCHA: A Semantics-Driven Framework for Natural Adversarial Example Generation },
author={ Xia Du and Xiaoyuan Liu and Jizhe Zhou and Zheng Lin and Chi-man Pun and Cong Wu and Tao Li and Zhe Chen and Wei Ni and Jun Luo },
journal={arXiv preprint arXiv:2506.10685},
year={ 2025 }
}