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We propose a physics-based authentication signature that real cameras produce naturally, but that generative models cannot faithfully reproduce. Our approach exploits the Moir\\'e effect: the interference fringes formed when a camera views a compact two-layer grating structure. We derive the Moir\\'e 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 w","title":"Moir\\'e Video Authentication: A Physical Signature Against AI Video Generation","url":"https://arxiv.org/abs/2604.01654","vendor":"arxiv_cs_ai"},"summary":"arXiv:2604.01654v2 Announce Type: replace-cross \nAbstract: 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 Moir\\'e effect: the interference fringes formed when a camera views a compact two-layer grating structure. We derive the Moir\\'e 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. 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