Moiré Video Authentication: A Physical Signature Against AI Video Generation

Recent advances in video generation have made AI-synthesizedcontent increasingly difficult to distinguish from real footage. We pro-pose a physics-based authentication signature that real cameras producenaturally, but that generative models cannot faithfully reproduce. Ourapproach exploits the Moiré effect: the interference fringes formed whena camera views a compact two-layer grating structure. We utilize theMoiré motion invariant as a verification criterion: under real image for-mation, fringe phase and grating image displacement are linearly cou-pled by optical geometry, independent of viewing distance and gratingstructure. A verifier extracts both signals from video and tests theircorrelation. We validate the invariant on both real-captured and AI-generated videos from multiple state-of-the-art generators, and find thatreal and AI-generated videos produce significantly different correlationsignatures, suggesting a robust means of differentiating them. Our workdemonstrates that deterministic optical phenomena can serve as physi-cally grounded, verifiable signatures against AI-generated video.