Detecting Diffusion-Generated Time Series Under Generator Shift
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Computer Science > Machine Learning
Title:Detecting Diffusion-Generated Time Series Under Generator Shift
Abstract:The boundary between real and diffusion-generated time series is becoming increasingly difficult to draw, yet detection in this domain remains underexplored, especially when the generator is unknown. We compare white-box detection, which requires access to the generator, against black-box detection, which operates on the raw signal alone. The white-box approach, a reconstruction-based detector adapted from the image domain, works well in in-distribution but breaks down under generator shift: reconstruction-based detection in images succeeds because large generic generators provide a near-universal reconstruction prior, and no analogous generator exists for time series. In contrast, a simple off-the-shelf classifier used as a black-box detector performs remarkably well, achieving an average F1 of 79.2, a 22.1% relative improvement over the white-box approach, and a TPR@1%FPR of 57.2. Diffusion-generated time series detection is therefore not a direct transfer of the image domain problem. This work provides the first systematic exploration of white-box and black-box detection for diffusion-generated time series. We close by identifying several open and promising directions.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.28355 [cs.LG] |
| (or arXiv:2605.28355v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.28355
arXiv-issued DOI via DataCite (pending registration)
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