Abstract
AI-generated videos have achieved near-perfect visual realism (e.g., Sora), urgently necessitating reliable detection mechanisms. However, detecting such videos faces significant challenges in modeling high-dimensional spatiotemporal dynamics and identifying subtle anomalies that violate physical laws. In this paper, we propose a physics-driven AI-generated video detection paradigm based on probability flow conservation principles. Specifically, we propose a statistic called Normalized Spatiotemporal Gradient (NSG), which quantifies the ratio of spatial probability gradients to temporal density changes, explicitly capturing deviations from natural video dynamics. Leveraging pre-trained diffusion models, we develop an NSG estimator through spatial gradients approximation and motion-aware temporal modeling without complex motion decomposition while preserving physical constraints. Building on this, we propose an NSG-based video detection method (NSG-VD) that computes the Maximum Mean Discrepancy (MMD) between NSG features of the test and real videos as a detection metric. Last, we derive an upper bound of NSG feature distances between real and generated videos, proving that generated videos exhibit amplified discrepancies due to distributional shifts. Extensive experiments confirm that NSG-VD outperforms state-of-the-art baselines by 16.00% in Recall and 10.75% in F1-Score, validating the superior performance of NSG-VD. The source code is available at this https URL.
Abstract (translated)
AI生成的视频已经达到了近乎完美的视觉逼真度（例如Sora），这迫切需要可靠的检测机制。然而，检测这些视频面临着巨大的挑战，包括建模高维时空动态和识别违反物理定律的细微异常。在这篇论文中，我们提出了一种基于概率流守恒原理的AI生成视频检测方法。具体来说，我们提出了一个名为归一化时空梯度（NSG）的统计量，该统计量量化了空间概率梯度与时间密度变化的比例，并且能够明确捕捉到自然视频动态中的偏差。通过利用预训练的扩散模型，我们在不进行复杂的运动分解的情况下开发了一种基于空间梯度近似和运动感知的时间建模的NSG估计器，同时保持物理约束。在此基础上，我们提出了一种基于NSG的视频检测方法（NSG-VD），该方法计算测试视频与真实视频之间的NSG特征的最大均值差异（MMD）作为检测指标。最后，我们推导出真实和生成视频之间NSG特征距离的一个上限，证明由于分布变化，生成的视频显示出放大了的偏差。广泛的实验确认了NSG-VD在召回率上比最先进的基准高出16.00%，在F1-Score上高出10.75%，验证了NSG-VD的优越性能。源代码可在该网址获得：[链接]（请根据实际论文中的信息添加正确的URL）。
URL
https://arxiv.org/abs/2510.08073
