Flow-Syndrome: Keyed Latent-Carrier Watermarking for Video Generation

The growing accessibility of realistic video generation creates an urgent need to preserve provenance through distribution and editing. Existing video watermarking methods encode information in output-space perturbations, generation noise, latent representations, or model parameters, each introducing different dependencies on generator access, inversion, or synchronization under temporal edits. We propose Flow-Syndrome, a generation-time watermarking framework that writes keyed code constraints into the latent representation of a frozen video generator and recovers them with a key-independent detector. A secret key determines a permutation and polarity map over detector carriers together with the keyed payload representation, while the detector itself is trained once per generator family and does not depend on the deployment key. During generation, bounded latent guidance drives detector scores toward the target carrier signs while fidelity and temporal-consistency terms limit perceptual distortion. At detection, the received video is re-encoded with the frozen VAE, carrier scores are aggregated over temporal groups, and the keyed mapping is inverted to recover the payload without access to the original video or inversion of the generative sampling process. Training through decode–transform–encode round trips, together with repeated encoding and temporal aggregation, improves robustness to common spatial, compression, and temporal edits. Experiments on both text-to-video and image-to-video models show strong payload recovery under diverse transformations while preserving video quality.