Spectral Tail Auxiliary Learning for AI-Generated Image Detection

TL;DR

This paper introduces Spectral Tail Auxiliary Learning (STAL), a novel frequency-domain method that improves generalization in AI-generated image detection by leveraging spectral tail cues without increasing inference complexity.

Contribution

The paper systematically analyzes spectral properties of generated images and proposes a new auxiliary learning framework that enhances detection robustness across diverse scenarios.

Findings

Spectral tail uplift is a common feature in generated images.

STAL achieves superior generalization across multiple datasets and models.

Abstract

As generative image models evolve rapidly, the perceptual gap between generated and real images continues to narrow, making AI-generated image detection increasingly challenging. Many existing methods exploit frequency-domain cues for detection, typically described as frequency-domain artifacts or high-frequency discrepancies. However, the specific and recurring spectral regularities remain insufficiently understood and characterized. In this paper, we systematically analyze the one-dimensional radial log-power spectra of real and generated images. We find that generated images do not necessarily exhibit higher or lower energy across the entire spectrum or high-band range. Instead, their spectra deviate from the power-law decay and show an anomalous uplift in the ultra-high-frequency tail. We term this phenomenon spectral tail uplift. We further attribute this phenomenon to nonlinear harmonic accumulation in trained generative models, suggesting that it can serve as a structural cue across generative architectures. Based on this observation, we propose Spectral Tail Auxiliary Learning (STAL), a frequency-domain auxiliary supervision framework for generalizable AI-generated image detection. STAL transfers spectral-tail cues from a tail-aware frequency teacher to a spatial detector during training, while all frequency-domain modules are discarded at inference time. Consequently, STAL introduces no inference overhead. Extensive experiments on 9 public datasets show that STAL achieves strong generalization and stability across generators, data distributions, and real-world scenarios.