FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error

TL;DR

This paper introduces FIRE, a frequency-based method that detects diffusion-generated images by analyzing reconstruction errors in frequency space, effectively distinguishing real from generated images even under various perturbations.

Contribution

FIRE is the first approach to leverage frequency decomposition in reconstruction error for robust detection of diffusion-generated images.

Findings

FIRE generalizes well to unseen diffusion models.

FIRE remains robust under diverse image perturbations.

FIRE outperforms existing detection methods.

Abstract

The rapid advancement of diffusion models has significantly improved high-quality image generation, making generated content increasingly challenging to distinguish from real images and raising concerns about potential misuse. In this paper, we observe that diffusion models struggle to accurately reconstruct mid-band frequency information in real images, suggesting the limitation could serve as a cue for detecting diffusion model generated images. Motivated by this observation, we propose a novel method called Frequency-guided Reconstruction Error (FIRE), which, to the best of our knowledge, is the first to investigate the influence of frequency decomposition on reconstruction error. FIRE assesses the variation in reconstruction error before and after the frequency decomposition, offering a robust method for identifying diffusion model generated images. Extensive experiments show that FIRE generalizes effectively to unseen diffusion models and maintains robustness against diverse perturbations.