Year: 2026
Type: conference-paper
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 timefrequency 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.
Authors: Zhichen Liu, Kaitong Qin, Linhan He, Yang Xu
Institution: Southern University of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166855600