AI 总览摘要
深度解读
通俗解读 非专业人士也能看懂
想象你在一个工厂里，工厂每天都在生产不同的产品。有些是自己生产的（真实视频），有些是外部公司用特殊技术制造的（AI生成视频）。工厂有很多检测工具，比如看产品的外观、检测机器的运动，但这些都很慢，而且需要很多资源。现在，工厂想用一种新方法，只看生产线上的运动信息（运动场），就能快速判断产品是不是由外部公司制造的。这就像用手边的运动信息（运动向量）作为线索，不用拆开每个产品（像素级分析），就能提前发现问题。这种方法简单、快速，还能保证误判率不变，就像在工厂里用一个聪明的门槛（阈值）控制所有检测。即使只看一部分信息，也能在很短时间内做出可靠判断，节省了大量资源。未来，这个方法还能结合其他线索，比如声音、温度，变得更聪明，帮助工厂更好地保护自己。
原文摘要
Detectors for AI-generated video are evaluated offline. A clip is decoded to pixels and scored once, increasingly by a large vision-language model. Detection, however, is deployed online. We recast the task as streaming perception and score the motion field the codec already wrote into the bitstream. Reading that field is a parse, not a pixel-domain forward pass. Because the running aggregate is monotone, one end-calibrated threshold is anytime-valid at the data-dependent decision time. Recalibrating at each prefix is not. Escalation is priced in closed form. A compute budget maps to a deferral window, on a frontier monotone exactly where the deferral condition holds. On matched GenVidBench the codec stage reaches full-length AUC 0.64 at five orders of magnitude less compute than a pixel CNN, on CPU. Its gate holds the stopping-time false-positive rate at target while the real data match its calibration, and drifts above it under distribution shift. Deferring 15% of clips lifts accuracy from 0.75 to 0.78 at $7\times$ less compute (paired: McNemar $p<10^{-6}$). The stage-1 ordering replicates on AIGVDBench. We introduce no new detector. The contribution is the reframing, two guarantees, and the measured frontiers. Code, configurations, and evaluation splits: https://github.com/KurbanIntelligenceLab/streamdet.