发表机构
University of Bologna（博洛尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究发现CLIP嵌入空间中人类与AI生成图像自发沿主导主方向分离，经分析排除多种直观解释，指向多尺度图像结构，揭示CLIP视觉证据与人类感知的差异，引发人工与人类视觉及审美关系的思考。
AI 中文摘要
我们在CLIP表示中发现了一种此前未被报道的现象：人类生成的绘画与AI生成的绘画在其联合嵌入分布的主导主方向上自发分离，且无需任何旨在区分这两类的监督目标。我们的目标并非利用该现象进行检测，而是对其进行解释：我们试图识别分离背后的视觉信息，并将其从嵌入空间追溯至图像域。我们通过结合可解释图像表示与基于梯度的逆变换（系统用作特征空间中已识别关系的实验探针）的渐进式研究来实现这一目标。鲁棒性实验及表达力日益增强的统计描述子逐步排除了基于全局图像属性和简单局部统计的若干直观解释，反而指向分布式多尺度图像结构。多尺度散射是所考虑的最具信息性的可解释表示，但仅能部分解释该现象。直接逆变换提供了一项互补且引人注目的观察结果：沿CLIP主导方向的显著位移可由人类观察者几乎察觉不到的图像扰动引发，表明分离涉及的方向对人类感知显著性极低的图像变化高度敏感。综合来看，这些结果揭示了CLIP表示中反映的视觉证据与人类易获取的视觉证据之间存在显著差异，引发了关于人工视觉与人类视觉之间关系，以及最终人工审美判断与人类审美判断之间关系的更广泛问题。
英文摘要
We identify a previously unreported phenomenon in CLIP representations: human and AI-generated paintings spontaneously separate along the dominant principal directions of their joint embedding distribution, without any supervised objective designed to distinguish the two classes. Rather than exploiting this phenomenon for detection, our objective is to interpret it: we seek to identify the visual information underlying the separation and to trace it back from the embedding space to the image domain. We pursue this objective through a progressive investigation combining interpretable image representations with gradient-based inversion, used systematically as an experimental probe of the relationships identified in feature space. Robustness experiments and increasingly expressive statistical descriptors progressively rule out several intuitive explanations based on global image properties and simple local statistics, and point instead to distributed multiscale image structure. Multiscale scattering provides the most informative interpretable representation considered, but offers only a partial account of the phenomenon. Direct inversion provides a complementary and striking observation: substantial displacements along the dominant CLIP directions can be induced by image perturbations that remain nearly imperceptible to human observers, showing that the directions involved in the separation are highly sensitive to image variations with very low perceptual salience for humans. Taken together, these results reveal a significant difference between the visual evidence reflected in CLIP representations and that readily accessible to human perception, raising broader questions about the relationship between artificial and human vision and, ultimately, between artificial and human aesthetic judgment.
