Perceptual Gaps: ASCII Art and Overlapping Audio as CAPTCHA

Computer Science > Cryptography and Security [Submitted on 4 Apr 2026] Perceptual Gaps: ASCII Art and Overlapping Audio as CAPTCHA Choon-Hou Rafael Chong As multimodal large language models (LLMs) advance, traditional CAPTCHAs have become obsolete at distinguishing humans from bots. To address this shift, this paper aims to investigate the possibility of using tasks for which humans have evolved highly specialised neural processing. We introduce two CAPTCHA classes: a vision-based CAPTCHA, which renders alphanumeric strings as ASCII art, and an audio-based CAPTCHA, which is a question-answering task with overlapping or noise-corrupted audio context. We evaluate our vision-based CAPTCHA both as text and image input with multiple frontier LLMs (GPT 5.2, Gemini 3, etc.), and assess our audio-based CAPTCHAs by applying augmentations like background noise, Gaussian noise, and overlapping speech. We determined that none of the LLMs were able to solve a single ASCII-based CAPTCHA, with the best performing model only being able to infer at most one or two characters. Additionally, all models that supported audio performed only modestly better than random when solving audio CAPTCHAs. Our results suggest that these CAPTCHAs are exceptionally effective today, but it is unclear whether it can withstand the fast-evolving landscape of artificial intelligence. Subsequent research is needed to determine whether these tasks are temporary vulnerabilities or represent a more durable method of distinguishing humans from bots. Comments: 8 pages, 3 figures. Research paper proposing novel CAPTCHA methods using ASCII art and overlapping audio Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.03612 [cs.CR]   (or arXiv:2604.03612v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.03612 Focus to learn more Submission history From: Choon-Hou Rafael Chong [view email] [v1] Sat, 4 Apr 2026 06:51:59 UTC (1,276 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)