提出新方法检测图像编辑中局部合理但全局矛盾的问题。
Common-Witness Certificates and Sharp Feature Bounds for Counterfactual Image Auditing
- 用公共见证证词和神经结构分析局部与全局一致性
- 在有限样本下给出特征值的精确边界和修复数量
- 适合关注图像真实性审计的研究者使用
图像编辑可能在每个区域都满足合理性约束，却仍不存在单一潜在解释能匹配整体输出。本文通过公共见证等级和见证神经结构形式化这一局部到全局的失效问题。该框架将审计与因果识别分离：仅需共享外生性即可允许任意区域边际耦合；而外部验证的见证关系则可生成预设图像特征的紧致部分识别边界。基于赫利型论证，提供针对拟凸损失、异质操作层级和有限见证图谱的简短不相容证书；块阻超图公式给出精确修复计数。对区域边际的联合置信区域实现完整识别区间的有限样本外覆盖。在可控MNIST、Morpho-MNIST和smallNORB上的实验验证了预测的局部-全局分离现象；合成实验测试了紧界、证书恢复与结构化计算。该方法用于审计声明的特征关系，不识别无限制的像素级反事实。
原文摘要 · Abstract (English)
An image editor may satisfy every regional plausibility constraint separately even when no single latent explanation fits the complete output. We formalize this local-to-global failure using a common witness grade and witness nerve. The framework separates auditing from causal identification: shared exogeneity alone allows every coupling of the regime marginals, whereas an externally justified witness relation yields sharp partial-identification bounds for prespecified image features. Helly-type arguments provide short incompatibility certificates for quasiconvex losses, heterogeneous action strata, and finite witness atlases; a blocker-hypergraph formula gives exact repair counts. Simultaneous confidence regions for the regime marginals give finite-sample outer coverage of the complete identified interval. Controlled MNIST, Morpho-MNIST, and smallNORB studies demonstrate the predicted local-global separation, while synthetic experiments test sharp bounds, certificate recovery, and structured computation. The method audits a declared feature relation and does not identify unrestricted pixel-level counterfactuals.
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