A Locally Tokenized Generative Model for Robust Time-Series Watermarking

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 detection requires each recovered unit to depend only on a bounded temporal neighborhood. Guided by this principle, we propose L-VQVAE, a generative model in which each discrete token is produced from a short contiguous window, and LVQMark, a watermarking method over this token space that combines logit-bias injection with robust re-encoding for attack-time detection. Experiments on four benchmarks spanning finance, energy, and neuroimaging show that our approach preserves generation quality while stabilizing both detection power and false-positive behavior under post-editing attacks.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

I.2.6; K.6.5; G.3

arXiv:2608.19727 [cs.LG]

(or arXiv:2608.19727v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.19727 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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