arXiv AI By Luyang Si, Leyi Pan, Dongsheng Ma, Lijie Wen

mAVE: A Watermark for Joint Audio-Visual Generation Models

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The paper introduces mAVE, a training‑free watermarking framework for joint audio‑visual diffusion transformers that binds audio and video modalities through a session‑keyed cryptographic digest. mAVE separates public record retrieval from secret session authentication, enabling provider‑assisted verification without altering generator weights or training auxiliary networks. Experiments on LTX‑2 and MOVA demonstrate high true‑positive rates (99.8%) and negligible false positives, while maintaining comparable generation quality.

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