Dynamic Optical Test for Bot Identification (DOT-BI): A simple check to identify bots in surveys and online processes
This addresses the problem of bot infiltration in surveys and online processes for researchers and platforms, though it appears incremental as it builds on existing CAPTCHA-like concepts with a new perceptual twist.
The paper tackles the problem of identifying bots in surveys and online processes by proposing DOT-BI, a method using human perception of motion to differentiate humans from automated systems, with results showing 99.5% of human participants solved the task in an average of 10.7 seconds while state-of-the-art models failed.
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.