不训练即可检测AI生成图像，速度快且准确率高。
Efficient Zero-Shot AI-Generated Image Detection
- 通过频域扰动敏感性分析识别图像细微伪造痕迹。
- 在OpenFake上比最先进方法AUC提升近10%，推理快1-2个数量级。
- 适合需要快速部署的图像真实性检测场景。
文本到图像模型的快速发展使生成图像愈发逼真，给内容真实性检测带来挑战。基于训练的检测器泛化能力有限，而无需训练的方法虽更鲁棒，却难以捕捉真实与合成图像间的细微差异。本文提出一种无需训练的AI生成图像检测方法，通过测量图像对结构化频率扰动的表示敏感性，实现对微小篡改的检测。该方法计算轻量，单张图像只需一次傅里叶变换即可生成扰动，推理速度比多数无训练检测器快1至2个数量级。在多个挑战性基准测试中，结果表明该方法优于现有最先进水平（SoTA）。尤其在OpenFake基准上，相比最先进方法，AUC提升近10%，同时保持显著更低的计算开销。
原文摘要 · Abstract (English)
The rapid progress of text-to-image models has made AI-generated images increasingly realistic, posing significant challenges for accurate detection of generated content. While training-based detectors often suffer from limited generalization to unseen images, training-free approaches offer better robustness, yet struggle to capture subtle discrepancies between real and synthetic images. In this work, we propose a training-free AI-generated image detection method that measures representation sensitivity to structured frequency perturbations, enabling detection of minute manipulations. The proposed method is computationally lightweight, as perturbation generation requires only a single Fourier transform for an input image. As a result, it achieves one to two orders of magnitude faster inference than most training-free detectors.Extensive experiments on challenging benchmarks demonstrate the efficacy of our method over state-of-the-art (SoTA). In particular, on OpenFake benchmark, our method improves AUC by nearly $10\%$ compared to SoTA, while maintaining substantially lower computational cost.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。