With the rapid advancement of video generation models such as Veo and Wan, the visual quality of synthetic content has reached a level where macro-level semantic errors and temporal inconsistencies are no longer prominent. However, this does not imply that the distinction between real and cutting-edge high-fidelity fake is untraceable. We argue that AI-generated videos are essentially products of a manifold-fitting process rather than a physical recording. Consequently, the pixel composition logic of consecutive adjacent frames residual in AI videos exhibits a structured and homogenous characteristic. We term this phenomenon `Manifold Projection Fluctuations' (MPF). Driven by this insight, we propose a hierarchical dual-path framework that operates as a sequential filtering process. The first, the Static Manifold Deviation Branch, leverages the refined perceptual boundaries of Large-Scale Vision Foundation Models (VFMs) to capture residual spatial anomalies or physical violations that deviate from the natural real-world manifold (off-manifold). For the remaining high-fidelity videos that successfully reside on-manifold and evade spatial detection, we introduce the Micro-Temporal Fluctuation Branch as a secondary, fine-grained filter. By analyzing the structured MPF that persists even in visually perfect sequences, our framework ensures that forgeries are exposed regardless of whether they manifest as global real-world manifold deviations or subtle computational fingerprints.

翻译：随着Veo和Wan等视频生成模型的快速发展，合成内容的视觉质量已达到宏观语义错误与时序不一致性不再突出的水平。然而，这并不意味着真实视频与前沿高保真伪造视频之间的差异不可追溯。我们认为AI生成视频本质上是流形拟合过程的产物而非物理录制的结果。因此，AI视频中连续相邻帧的像素组合逻辑呈现出结构化且同质化的特征。我们将此现象命名为“流形投影波动”。基于这一洞见，我们提出一种分层双路径框架，其作为序列过滤流程运行。第一路径——静态流形偏差分支——利用大规模视觉基础模型的精细化感知边界，捕捉偏离自然真实世界流形的残留空间异常或物理违例。对于成功驻留于流形并逃避空间检测的剩余高保真视频，我们引入微时序波动分支作为二级细粒度过滤器。通过分析即使在视觉完美序列中仍持续存在的结构化流形投影波动，本框架确保无论伪造表现为全局真实世界流形偏差还是细微的计算指纹，均能被有效揭露。