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.

翻译：扩散模型的快速发展显著提升了高质量图像生成能力，使得生成内容与真实图像越来越难以区分，并引发了对其潜在滥用的担忧。本文观察到，扩散模型在重构真实图像的中频带信息时存在困难，表明这一局限性可作为检测扩散模型生成图像的线索。基于此观察，我们提出了一种名为频率引导重构误差（FIRE）的新方法。据我们所知，这是首次探究频率分解对重构误差影响的研究。FIRE通过评估频率分解前后重构误差的变化，为识别扩散模型生成的图像提供了一种鲁棒的方法。大量实验表明，FIRE能有效泛化至未见过的扩散模型，并在多种扰动下保持鲁棒性。