Detecting Diffusion-Generated Time Series Under Generator Shift

2026-05-27

AI Summary

This study addresses the challenge of detecting time series generated by diffusion models when the generator is unknown and distribution shifts are present. Existing reconstruction-based white-box detection methods struggle in this setting due to the absence of universal generative priors for time series, a fundamental distinction from the image domain. The work presents the first systematic comparison between white-box and black-box detection paradigms for this task, demonstrating that off-the-shelf black-box classifiers significantly outperform white-box approaches. Empirical results across multiple datasets show that black-box methods achieve an average F1 score of 79.2%, representing a relative improvement of 22.1%, and attain a true positive rate of 57.2% at a 1% false positive rate, thereby validating the effectiveness and superiority of the black-box strategy.

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.

Problem

• diffusion-generated time series
• generator shift
• time series detection
• black-box detection
• white-box detection

Innovation

• diffusion-generated time series
• generator shift
• black-box detection
• white-box detection
• time series anomaly detection