利用频率重建误差检测扩散生成图像，有效区分真假图片。
FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error
- 通过频域分解分析重建误差差异，发现生成图像在中频段重建不佳。
- 在多个数据集上对未见生成模型的检测准确率超90%。
- 适合需要防伪的图像审核、内容验证等场景使用。
扩散模型的快速发展极大提升了图像生成质量，使得生成内容越来越难以与真实图像区分，引发滥用担忧。本文观察到扩散模型在真实图像的中频段频率信息重建上存在局限，这一缺陷可作为检测生成图像的线索。受此启发，我们提出一种新方法——频率引导重建误差（FIRE），据我们所知，这是首个系统研究频域分解对重建误差影响的方法。FIRE通过比较频率分解前后重建误差的变化，实现对扩散模型生成图像的鲁棒识别。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稳定性。
原文摘要 · Abstract (English)
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.
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