利用物理莫尔效应差异，区分真实视频与AI生成视频。
Moiré Video Authentication: A Physical Signature Against AI Video Generation
- 基于相机拍摄时产生的莫尔条纹相位与位移线性关系，构建认证信号。
- 真实视频莫尔信号相关性高，AI生成视频相关性显著偏低。
- 适合需要防伪的视频真实性验证场景，如新闻、司法取证。
近期视频生成技术发展迅速，使AI合成内容越来越难以与真实影像区分。本文提出一种基于物理现象的认证签名：真实相机自然产生，而生成模型无法忠实复现的莫尔效应。该效应是当相机拍摄双层光栅结构时形成的干涉条纹。我们推导出莫尔运动不变性，证明条纹相位与光栅图像位移由光学几何决定，与拍摄距离和光栅结构无关。验证器从视频中提取两者信号并检测其相关性。在多个主流生成模型生成的视频及真实拍摄视频上测试，结果显示真实与生成视频的相关性存在显著差异，表明该方法能有效区分二者。研究证明，确定性光学现象可作为可验证的物理签名，用于对抗AI生成视频。
原文摘要 · Abstract (English)
Recent advances in video generation have made AI-synthesized content increasingly difficult to distinguish from real footage. We propose a physics-based authentication signature that real cameras produce naturally, but that generative models cannot faithfully reproduce. Our approach exploits the Moiré effect: the interference fringes formed when a camera views a compact two-layer grating structure. We derive the Moiré motion invariant, showing that fringe phase and grating image displacement are linearly coupled by optical geometry, independent of viewing distance and grating structure. A verifier extracts both signals from video and tests their correlation. We validate the invariant on both real-captured and AI-generated videos from multiple state-of-the-art generators, and find that real and AI-generated videos produce significantly different correlation signatures, suggesting a robust means of differentiating them. Our work demonstrates that deterministic optical phenomena can serve as physically grounded, verifiable signatures against AI-generated video.
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