Domain-Shift

WaveDetect: Wavelet-based spectral fingerprinting for robust machine-generated text detection

WaveDetect is a new framework that reframes LLM-generated text detection as a signal processing problem, applying a differentiable Continuous Wavelet Transform to token probability sequences to extract 'spectral fingerprints' invisible in the time domain. The approach targets three known failure modes of existing detectors: adversarial perturbations, cross-domain shifts, and temporal model evolution. Evaluations on RAID, EvoBench, and Domain-Shift benchmarks claim state-of-the-art accuracy and robustness against sophisticated attacks and unseen LLMs.