# SpecDetect: Simple, Fast, and Training-Free Detection of LLM-Generated Text via Spectral Analysis

arXiv: https://arxiv.org/abs/2508.11343 · HTML: https://mlsanity.com/paper/2508.11343/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2025.

## Built on by (most cited first, 6 of 6)

- [Log-Likelihood, Simpson's Paradox, and the Detection of Machine-Generated Text](https://mlsanity.com/paper/2605.06294/) (2026)
- [Steer-to-Detect: Probing Hidden Representations for Detection of LLM-Generated Texts](https://mlsanity.com/paper/2605.12890/) (2026)
- [DWT-Fusion: A Signal-Based Framework for Training-Free LLM-Generated Text Detection](https://mlsanity.com/paper/2607.22026/) (2026)
- [C-HAT-Bench: Benchmarking Chinese AI-Text Detection Beyond Fully Generated Text](https://mlsanity.com/paper/2609.32770/) (2026)
- [Using LLMs to Detect LLM-Generated Texts: A Cross-Generation Analysis](https://mlsanity.com/paper/2609.34691/) (2026)
- [Belief-Trajectory Energy: Measuring the Path to a Prediction](https://mlsanity.com/paper/2610.05114/) (2026)

## Abstract (arXiv)

The proliferation of high-quality text from Large Language Models (LLMs) demands reliable and efficient detection methods. While existing training-free approaches show promise, they often rely on surface-level statistics and overlook fundamental signal properties of the text generation process. In this work, we reframe detection as a signal processing problem, introducing a novel paradigm that analyzes the sequence of token log-probabilities in the frequency domain. By systematically analyzing the signal's spectral properties using the global Discrete Fourier Transform (DFT) and the local Short-Time Fourier Transform (STFT), we find that human-written text consistently exhibits significantly higher spectral energy. This higher energy reflects the larger-amplitude fluctuations inherent in human writing compared to the suppressed dynamics of LLM-generated text. Based on this key insight, we construct SpecDetect, a detector built on a single, robust feature from the global DFT: DFT total energy. We also propose an enhanced version, SpecDetect++, which incorporates a sampling discrepancy mechanism to further boost robustness. Extensive experiments show that our approach outperforms the state-of-the-art model while running in nearly half the time. Our work introduces a new, efficient, and interpretable pathway for LLM-generated text detection, showing that classical signal processing techniques offer a surprisingly powerful solution to this modern challenge.