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---
title: "预测不变区间 (Prediction-Invariant Intervals)"
created: 2026-07-04
updated: 2026-07-04
type: concept
tags: [certification, interval, prediction, semantics]
sources: ["arXiv:2606.18839"]
---
# 预测不变区间 (Prediction-Invariant Intervals)
语义鲁棒性认证的输出:将语义 extent 范围 $[\varphi_a, \varphi_{a'}]$ 切分为若干子区间,在每个区间内 VLM 预测类别保持不变。
## 计算过程
1. **闭式 margin**:将语义变换 $\gamma(\varphi)$ 代入 pairwise margin
$$m_{c,c'}(\varphi) = A_{c,c'} \cos\varphi + B_{c,c'} \sin\varphi + C_{c,c'}$$
2. **求解穿越点**$m_{c,c'}(\varphi) = 0$ 的闭式解
3. **收集排序**:所有类别对的穿越点集合 $\{\varphi_\ell\}_{\ell=0}^L$
4. **区间划分**$(\varphi_\ell, \varphi_{\ell+1})$ 内预测恒定
## 证书输出
$$\mathcal{S} = \{((\varphi_\ell, \varphi_{\ell+1}), y_\ell) : \ell = 0, \ldots, L-1\}$$
- 每个区间标注预测类别 $y_\ell$
- 区间边界对应类别翻转点class flip
## 聚合指标:预测不变概率
$$\mathbb{P}(f(\gamma(\varphi)) = \hat{y}) = \frac{1}{\varphi_{a'} - \varphi_a} \sum_{\ell: y_\ell = \hat{y}} (\varphi_{\ell+1} - \varphi_\ell)$$
度量整个语义 extent 范围内预测保持不变的**总比例**。
## 参考
- [[semantic-robustness-certification|语义鲁棒性认证]]
- [[semantic-extent|语义 extent]]
- [[voronoi-decision-regions|Voronoi 决策区域]]
- [[semantic-robustness-certification-vlm-2026|论文原文]]