Dynamic Optical Test for Bot Identification (DOT-BI): A simple check to identify bots in surveys and online processes

TL;DR

The paper introduces DOT-BI, a simple optical test leveraging human perception of motion to distinguish humans from bots in online surveys, effectively resisting advanced automated detection methods.

Contribution

It presents a novel optical test that is easy to implement and effective against state-of-the-art AI models for bot detection in online environments.

Findings

99.5% human participants solved the task

AI models failed to extract correct values

No negative effects on user experience

Abstract

We propose the Dynamic Optical Test for Bot Identification (DOT-BI): a quick and easy method that uses human perception of motion to differentiate between human respondents and automated systems in surveys and online processes. In DOT-BI, a 'hidden' number is displayed with the same random black-and-white pixel texture as its background. Only the difference in motion and scale between the number and the background makes the number perceptible to humans across frames, while frame-by-frame algorithmic processing yields no meaningful signal. We conducted two preliminary assessments. Firstly, state-of-the-art, video-capable, multimodal models (GPT-5-Thinking and Gemini 2.5 Pro) fail to extract the correct value, even when given explicit instructions about the mechanism. Secondly, in an online survey (n=182), 99.5% (181/182) of participants solved the task, with an average end-to-end completion time of 10.7 seconds; a supervised lab study (n=39) found no negative effects on perceived ease-of-use or completion time relative to a control. We release code to generate tests and 100+ pre-rendered variants to facilitate adoption in surveys and online processes.