用任意数据源训练，提升检测AI生成图像的泛化能力
Leveraging Arbitrary Data Sources for AI-Generated Image Detection Without Sacrificing Generalization
- 构建单类归因空间，放大真实与生成图像差异
- 在未见生成模型上准确率提升7.21%，泛化性能提升7.20%
- 无需依赖特定生成器特征，适合跨模型检测场景
生成模型的快速演进给AI生成图像检测带来新挑战，尤其在现实场景中新型生成技术不断涌现。现有学习范式易导致分类器依赖训练数据，决策边界狭窄，泛化能力受限。我们观察到真实图像与生成图像在预训练视觉编码器提取的高维特征空间中均呈现簇状低维流形结构。基于此，提出一种单类归因建模框架：先从任意单类训练集（真实或生成图像）构建紧凑归因空间，放大两类间差异；再在此基础上建立更稳定的决策边界，增强类别区分度，减少对生成器特有痕迹的依赖，从而提升跨模型泛化能力。大量实验表明，该方法在多种未见生成模型上表现优异，准确率最高提升7.21%，跨模型泛化性能提升7.20%。
原文摘要 · Abstract (English)
The accelerating advancement of generative models has introduced new challenges for detecting AI-generated images, especially in real-world scenarios where novel generation techniques emerge rapidly. Existing learning paradigms are likely to make classifiers data-dependent, resulting in narrow decision margins and, consequently, limited generalization ability to unseen generative models. We observe that both real and generated images intend to form clustered low-dimensional manifolds within high-level feature spaces extracted by pre-trained visual encoders. Building on this observation, we propose a single-class attribution modeling framework that first amplifies the intrinsic differences between real and generated images by constructing a compact attribution space from any single-class training set, either composed of real images or generated ones, and then establishes a more stable decision boundary upon the enlarged separation. This process enhances class distinction and mitigates the reliance on generator-specific artifacts, thereby improving cross-model generalization. Extensive experiments show that our method generalizes well across various unseen generative models, outperforming existing detectors by as much as 7.21% in accuracy and 7.20% in cross-model generalization.
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