Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream
The paper proposes a novel approach for detecting AI-generated videos in real-time by analyzing the compressed bitstream instead of decoding to pixels. It introduces a streaming perception framework that uses motion field data from the codec, enabling anytime detection with a single calibrated threshold. The method achieves 0.64 AUC on GenVidBench with five orders of magnitude less compute than pixel-based CNNs, and a deferral strategy improves accuracy from 0.75 to 0.78 while reducing compute by 7x.
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[Submitted on 21 Jul 2026]
Title:Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream
View a PDF of the paper titled Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream, by Mert Onur Cakiroglu and 2 other authors
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Abstract: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
new | recent | 2026-07
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