arXiv: cs.AI preprintsInternational6 November 2026
A Locally Tokenized Generative Model for Robust Time-Series Watermarking
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arXiv:2608.19727v2 Announce Type: replace-cross Abstract: Watermarking is a central tool for provenance in generative models, yet its application to multivariate time series remains hindered by reliability failures under post-editing attacks. We show that existing detectors, which rely on globally coupled re-encoding, suffer from bidirectional drift of the null distribution: post-editing attacks can shift the $z$-score of non-watermarked samples in either direction, invalidating clean-calibrated thresholds. We argue that this instability is a property of the re-encoding, and that reliable dete
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