Kavli Affiliate: Ke Wang
| First 5 Authors: Lehao Lin, Ke Wang, Maha Abdallah, Wei Cai,
| Summary:
In recent years, the rapid development of artificial intelligence (AI)
especially multi-modal Large Language Models (MLLMs), has enabled it to
understand text, images, videos, and other multimedia data, allowing AI systems
to execute various tasks based on human-provided prompts. However, AI-powered
bots have increasingly been able to bypass most existing CAPTCHA systems,
posing significant security threats to web applications. This makes the design
of new CAPTCHA mechanisms an urgent priority. We observe that humans are highly
sensitive to shifts and abrupt changes in videos, while current AI systems
still struggle to comprehend and respond to such situations effectively. Based
on this observation, we design and implement BounTCHA, a CAPTCHA mechanism that
leverages human perception of boundaries in video transitions and disruptions.
By utilizing generative AI’s capability to extend original videos with prompts,
we introduce unexpected twists and changes to create a pipeline for generating
guided short videos for CAPTCHA purposes. We develop a prototype and conduct
experiments to collect data on humans’ time biases in boundary identification.
This data serves as a basis for distinguishing between human users and bots.
Additionally, we perform a detailed security analysis of BounTCHA,
demonstrating its resilience against various types of attacks. We hope that
BounTCHA will act as a robust defense, safeguarding millions of web
applications in the AI-driven era.
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