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TimeWak: Temporal Chained-Hashing Watermark for Time Series Data
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Proposes TimeWak, a generation-time watermark for multivariate time series diffusion models that embeds temporally chained-hashed seeds in data space and detects them via epsilon-exact BDIA-DDIM inversion.
Problem
Watermarking synthetic multivariate time series produced by data-space diffusion models, where latent-space watermarks are inapplicable and feature heterogeneity and temporal dependence make detection hard under lossy diffusion inversion.
Approach
Embed watermark seeds as temporally chained hashes along the time axis, shuffle them per feature, map them to initial Gaussian noise, and detect by inverting a BDIA-DDIM sampler with a bounded inversion error (epsilon-exact inversion) and checking hash consistency.
Key results
- TimeWak improves Context-FID by 61.96% on MuJoCo versus the strongest baseline TabWak-transposed.
- TimeWak improves correlational score by 8.44% on fMRI and reaches TPR 1.0 at 0.1% FPR on 64-length sequences with one or two samples.
- Under post-editing attacks TimeWak keeps the best Z-scores in most settings, e.g., 375.96 under 5% offset on Stocks, while TabWak variants and HTW degrade.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TimeWak instead of TabWakᵀ | Diffusion-TS watermarking · MuJoCo | Context-FID: 61.96% better | Helped |
| Use TimeWak instead of TabWakᵀ | Diffusion-TS watermarking · fMRI | Correlational: 8.44% better | Helped |
| Use TimeWak instead of the strongest baseline | Diffusion-TS watermarking · Stocks, ETTh, MuJoCo, Energy and fMRI | Z-score | Mixed |
Surprising findings
- Tree-Ring's detection scores increase under some post-processing attacks such as random cropping and min-max insertion.
- HTW sometimes beats un-watermarked data on quality metrics, possibly because its perturbations move samples closer to ground truth, yet it has low Z-scores.
Limitations
- Does not natively support streaming data; requires windowed processing.
- Per-timestep watermarking remains unexplored.
- Epsilon-exact inversion is an approximation that sets x1 to x0, so inversion is not exact.
Experimental setup
Datasets: Stocks ETTh MuJoCo Energy fMRI
Benchmarks: Stocks ETTh MuJoCo Energy fMRI
Metrics: Context-FID Correlational score Discriminative score Predictive score Z-score TPR@0.1%FPR
Baselines: Tree-Ring Gaussian Shading TabWak TabWak-transposed Heads Tails Watermark
Builds on: Diffusion-TS DDIM BDIA Gaussian Shading TabWak
Closest papers by meaning
- A Locally Tokenized Generative Model for Robust Time-Series Watermarking (NeurIPS 2026 · similarity 0.88)
- Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion Models (CVPR 2025 · similarity 0.86)
- SleeperMark: Towards Robust Watermark against Fine-Tuning Text-to-image Diffusion Models (CVPR 2025 · similarity 0.85)
- T2SMark: Balancing Robustness and Diversity in Noise-as-Watermark for Diffusion Models (NeurIPS 2025 · similarity 0.85)
- Shallow Diffuse: Robust and Invisible Watermarking through Low-Dim Subspaces in Diffusion Models (NeurIPS 2025 · similarity 0.84)
- KGMark: A Diffusion Watermark for Knowledge Graphs (ICML 2025 · similarity 0.84)
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