用高斯判别器分析生成图像检测的泛化瓶颈，发现先验影响关键。
Prior-Conditioned Gaussian Discriminants for Generalizable AI-generated Image Detection
- 基于特征统计构建可解析的高斯判别阶梯，无需训练头
- 在39个数据集上，性能常媲美甚至超越现成检测器
- 揭示先验敏感性与特征依赖性，推动更透明的评估
基于扩散模型的生成图像已无处不在，但现有检测器在生成器、提示词/风格和源域同时变化时往往失效。本文将生成图像检测建模为由训练先验、冻结编码器特征空间和决策规则组成的迁移系统，并探讨分类头训练是否能超越现代特征本身已具备的可分性。作为可控诊断，我们构建了先验条件化的高斯判别阶梯：在嵌套协方差假设下，利用一阶与二阶特征统计构造闭式分类头。在覆盖39个公开数据集（共710万张图像）的Percept-Lens统一评测协议中，最优层级的表现通常与已发布的检测器相当，甚至在匹配先验和编码器的情况下有所超越。进一步量化了训练先验的强敏感性、基于矩的分类头的数据效率，以及高斯迁移度量对表示的依赖性，呼吁在（先验、编码器、分类头）层面进行报告，并建立更强的分析基线以支持AIGI迁移研究。
原文摘要 · Abstract (English)
Diffusion-based generators have made synthetic images ubiquitous, but detectors often fail under simultaneous shifts in generator, prompt/style, and source-domain. We study AI-generated image detection as a transfer system described by training prior, frozen encoder feature space, and decision rule, and ask when classifier head training adds value beyond what is already separable in modern features. As a controlled diagnostic, we fit a prior-conditioned Gaussian discriminant ladder: closed-form heads built from first- and second-order feature statistics under nested covariance assumptions. On Percept-Lens, a unified protocol over 39 public datasets (7.1 million images), the best rung is frequently competitive with, and sometimes exceeds, released AI-generated image detector heads when matched on both prior and encoder. We further quantify strong sensitivity to the training prior, data-efficiency of moment-based heads, and representation dependence of Gaussian shift metrics, motivating (prior, encoder, head)-level reporting and stronger analytical baselines for AIGI transfer.
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