dorsal/arxiv
View SchemaRobust CAPTCHA Using Audio Illusions in the Era of Large Language Models: from Evaluation to Advances
| Authors | Ziqi Ding, Yunfeng Wan, Wei Song, Yi Liu, Gelei Deng, Nan Sun, Huadong Mo, Jingling Xue, Shidong Pan, Yuekang Li |
|---|---|
| Categories | |
| ArXiv ID | 2601.08516vv1 |
| URL | https://arxiv.org/abs/2601.08516 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
CAPTCHAs are widely used by websites to block bots and spam by presenting challenges that are easy for humans but difficult for automated programs to solve. To improve accessibility, audio CAPTCHAs are designed to complement visual ones. However, the robustness of audio CAPTCHAs against advanced Large Audio Language Models (LALMs) and Automatic Speech Recognition (ASR) models remains unclear. In this paper, we introduce AI-CAPTCHA, a unified framework that offers (i) an evaluation framework, ACEval, which includes advanced LALM- and ASR-based solvers, and (ii) a novel audio CAPTCHA approach, IllusionAudio, leveraging audio illusions. Through extensive evaluations of seven widely deployed audio CAPTCHAs, we show that most existing methods can be solved with high success rates by advanced LALMs and ASR models, exposing critical security weaknesses. To address these vulnerabilities, we design a new audio CAPTCHA approach, IllusionAudio, which exploits perceptual illusion cues rooted in human auditory mechanisms. Extensive experiments demonstrate that our method defeats all tested LALM- and ASR-based attacks while achieving a 100% human pass rate, significantly outperforming existing audio CAPTCHA methods.
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"date_created": "2026-02-17T05:53:15.226000Z",
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"abstract": "CAPTCHAs are widely used by websites to block bots and spam by presenting challenges that are easy for humans but difficult for automated programs to solve. To improve accessibility, audio CAPTCHAs are designed to complement visual ones. However, the robustness of audio CAPTCHAs against advanced Large Audio Language Models (LALMs) and Automatic Speech Recognition (ASR) models remains unclear.\n In this paper, we introduce AI-CAPTCHA, a unified framework that offers (i) an evaluation framework, ACEval, which includes advanced LALM- and ASR-based solvers, and (ii) a novel audio CAPTCHA approach, IllusionAudio, leveraging audio illusions. Through extensive evaluations of seven widely deployed audio CAPTCHAs, we show that most existing methods can be solved with high success rates by advanced LALMs and ASR models, exposing critical security weaknesses.\n To address these vulnerabilities, we design a new audio CAPTCHA approach, IllusionAudio, which exploits perceptual illusion cues rooted in human auditory mechanisms. Extensive experiments demonstrate that our method defeats all tested LALM- and ASR-based attacks while achieving a 100% human pass rate, significantly outperforming existing audio CAPTCHA methods.",
"arxiv_id": "2601.08516",
"authors": [
"Ziqi Ding",
"Yunfeng Wan",
"Wei Song",
"Yi Liu",
"Gelei Deng",
"Nan Sun",
"Huadong Mo",
"Jingling Xue",
"Shidong Pan",
"Yuekang Li"
],
"categories": [
"cs.SD",
"cs.CY",
"eess.AS"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Robust CAPTCHA Using Audio Illusions in the Era of Large Language Models: from Evaluation to Advances",
"url": "https://arxiv.org/abs/2601.08516",
"version": "v1"
},
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"variant": "snapshot-2026-01-17",
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