AI-Generated Text is Non-Stationary: Detection via Temporal Tomography

• URL: http://arxiv.org/abs/2508.01754v1
• Date: Sun, 03 Aug 2025 13:43:34 GMT
• Title: AI-Generated Text is Non-Stationary: Detection via Temporal Tomography
• Authors: Alva West, Yixuan Weng, Minjun Zhu, Luodan Zhang, Zhen Lin, Guangsheng Bao, Yue Zhang,
• Abstract summary: We introduce Temporal Discrepancy Tomography (TDT), a novel detection paradigm that preserves positional information by reformulating detection as a signal processing task.<n>On the RAID benchmark, TDT achieves 0.855 AUROC (7.1% improvement over the best baseline)<n>Our work establishes non-stationarity as a fundamental characteristic of AI-generated text and demonstrates that preserving temporal dynamics is essential for robust detection.
• Score: 22.431500389494836
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of AI-generated text detection has evolved from supervised classification to zero-shot statistical analysis. However, current approaches share a fundamental limitation: they aggregate token-level measurements into scalar scores, discarding positional information about where anomalies occur. Our empirical analysis reveals that AI-generated text exhibits significant non-stationarity, statistical properties vary by 73.8\% more between text segments compared to human writing. This discovery explains why existing detectors fail against localized adversarial perturbations that exploit this overlooked characteristic. We introduce Temporal Discrepancy Tomography (TDT), a novel detection paradigm that preserves positional information by reformulating detection as a signal processing task. TDT treats token-level discrepancies as a time-series signal and applies Continuous Wavelet Transform to generate a two-dimensional time-scale representation, capturing both the location and linguistic scale of statistical anomalies. On the RAID benchmark, TDT achieves 0.855 AUROC (7.1\% improvement over the best baseline). More importantly, TDT demonstrates robust performance on adversarial tasks, with 14.1\% AUROC improvement on HART Level 2 paraphrasing attacks. Despite its sophisticated analysis, TDT maintains practical efficiency with only 13\% computational overhead. Our work establishes non-stationarity as a fundamental characteristic of AI-generated text and demonstrates that preserving temporal dynamics is essential for robust detection.

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