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

arXiv: https://arxiv.org/abs/2512.03580 · HTML: https://mlsanity.com/paper/2512.03580/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 3 of 3)

- [Humanity's Last Exam](https://mlsanity.com/paper/2501.14249/) (2025)
- [Detecting the Use of Generative AI in Crowdsourced Surveys: Implications for Data Integrity](https://mlsanity.com/paper/2510.24594/) (2025)
- [Is Crowdsourcing a Puppet Show? Detecting a New Type of Fraud in Online Platforms](https://mlsanity.com/paper/2511.00195/) (2025)

## Abstract (arXiv)

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.