尽早检测，极少升级：从压缩比特流中随时检测人工智能生成的视频
Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream
浏览论文内容
中文总结 AI 辅助
研究从压缩比特流中随时检测人工智能生成视频的问题，核心方法是将任务重定义为流感知并对运动场评分，贡献是重新构建、两个保证及测量前沿，在GenVidBench等上取得较好效果。
中文摘要 AI 辅助
人工智能生成视频的检测器是离线评估的。将一个片段解码为像素并一次性评分，由一个大型视觉语言模型不断提高评分。然而，检测是在线部署的。我们将该任务重新定义为流感知，并对编解码器已经写入比特流中的运动场进行评分。读取该场是一种解析，而不是像素域的前向传递。由于运行总和是单调的，一个端点校准阈值在数据依赖的决策时间是随时有效的。在每个前缀处重新校准则不然。升级是以封闭形式定价的。计算预算映射到一个延迟窗口，在前沿处正好在延迟条件成立的地方单调。在匹配的GenVidBench上，编解码器阶段在CPU上达到全长AUC 0.64，计算量比像素CNN少五个数量级。其门控在真实数据符合校准时将停止时间的误报率保持在目标水平，而在分布变化时则高于该水平。推迟15%的片段可将准确率从0.75提高到0.78，计算量减少7倍（配对：McNemar p<10⁻⁶）。第一阶段的排序在AIGVDBench上重复。我们没有引入新的检测器。贡献在于重新构建、两个保证和测量的前沿。代码、配置和评估分割：此https URL。
英文摘要
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.
