SPLIT: Training-Free AI-Generated and Partially Edited Video Detection via Spatial Patch‑Level Incoherence and Temporal Roughness

Deploying AI-generated video detectors in real-world ser-vices demands an ultra-low false positive rate (FPR) on real videosto avoid falsely rejecting authentic content, a regime where standardmetrics such as AUROC fail to reflect actual operating behavior. Weintroduce Spatial Patch-Level Incoherence and Temporal Roughness(SPLIT), a training-free detector that operates on patch tokens froma frozen vision encoder to detect both fully generated and partiallyedited videos. SPLIT computes two complementary signals: Two-stepTemporal Roughness (TTR), capturing non-smooth patch trajectoriesvia one-step and two-step feature variation contrast, and Local SpatialMotion Incoherence (LSMI), measuring spatially inconsistent temporalchanges through gradients of a feature-space motion field. The two arefused multiplicatively with gamma correction to sharpen real–fake sepa-ration at strict thresholds. We further propose a service-aligned evalua-tion protocol based on Fake Recall at fixed FPR with real-only thresholdcalibration and cross-real threshold transfer. Across three benchmarks—FakeParts, GenVideo, and ViF-Bench—SPLIT achieves the highest FakeRecall at FPR = 0.1%, substantially outperforming supervised and training-free baselines while remaining robust to post-processing with negligi-ble overhead. The code is publicly available at https://github.com/mldljyh/SPLIT.