用动态视觉差异识别问卷中的机器人，人类秒认，机器却看不见。
Dynamic Optical Test for Bot Identification (DOT-BI): A simple check to identify bots in surveys and online processes
- 利用人眼对运动和尺度变化的感知差异设计隐蔽数字测试
- 99.5%人类参与者10.7秒内完成，顶尖模型全数失败
- 适合用于在线调查与流程防伪，代码已开源
我们提出动态光学检测机器人识别方法（DOT-BI）：一种快速简便的方法，通过人类对运动的感知差异区分真人与自动化系统。在DOT-BI中，一个‘隐藏’数字以与背景相同的随机黑白像素纹理呈现，仅因数字与背景在运动和尺度上的差异，人类可在多帧间感知到该数字，而逐帧算法处理无法提取有效信号。我们进行了两项初步评估：第一，当前最先进的视频能力多模态模型（GPT-5-Thinking 和 Gemini 2.5 Pro）即使明确提示机制也无法正确识别数值；第二，在在线调查中（n=182），99.5%（181/182）参与者成功完成任务，平均耗时10.7秒；在受控实验室研究中（n=39），与对照组相比，无显著影响感知易用性或完成时间。我们公开了生成测试的代码及100多个预渲染变体，以促进在调查与在线流程中的应用。
原文摘要 · Abstract (English)
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.
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