WaveDetect: Robust Framework for Machine-Generated Text Detection via Wavelet Transform

• URL: http://arxiv.org/abs/2606.23336v1
• Date: Mon, 22 Jun 2026 13:42:36 GMT
• Title: WaveDetect: Robust Framework for Machine-Generated Text Detection via Wavelet Transform
• Abstract summary: Existing detectors often falter when facing three critical challenges: adversarial perturbations, cross-domain shifts, and the rapid temporal evolution of the foundation model.<n>We propose wavedetect, a novel framework that reformulates text detection as a signal processing task within the time-frequency domain.
• Score: 2.7227283756285288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Large Language Models asymptotically approach human-level fluency in natural language generation, solely relying on surface-level semantic artifacts for detecting LLM-generated texts has become increasingly precarious. Existing detectors often falter when facing three critical challenges: adversarial perturbations, cross-domain shifts, and the rapid temporal evolution of the foundation model. To address these issues, we propose \wavedetect, a novel framework that reformulates text detection as a signal processing task within the time-frequency domain. Unlike previous methods that analyze static token probability distributions, \wavedetect models the generated output as a probability signal, upon which a differentiable Continuous Wavelet Transform is applied to convert them into learnable spectral representations. This process reveals the intrinsic ``spectral fingerprints'' in machine-generated texts--patterns that remain invisible in time domain. Comprehensive evaluations on three well-curated datasets (RAID, EvoBench, and Domain-Shift) show that our method achieves a new state-of-the-art. It not only achieves superior accuracy but also exhibits remarkable robustness against sophisticated attacks, generalization across out-of-distribution topics and unseen evolving LLMs. Our results validate the efficacy of spectral analysis as a promising paradigm for LLM-generated texts detection.

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