通过可解释的最小修改，同时诊断分类错误并评估模型鲁棒性。
Optimized Instance Alteration for Explaining and Assessing Robustness of Classifiers
- 用稀疏可解释的修改优化实例，让模型输出目标标签。
- 提出容忍区域混淆矩阵，量化分类器在扰动下的误判概率。
- 适用于图像和表格数据，兼顾解释性与鲁棒性分析。
本文提出一种统一方法，用于诊断黑箱分类器的误分类并评估其鲁棒性。核心是优化框架：在保证修改结果可解释的前提下，使分类器对扰动后的实例输出指定目标标签。目标函数包含两部分：解释性感知的 $L_0$（XA-$L_0$）正则项，促进稀疏且可解释的修改；以及分类器损失项，引导扰动实例向期望输出靠拢。该优化框架既用于揭示误分类的根本原因，也用于评估鲁棒性——即实例在容忍区域内如何变化后被重新分配到其他类别。为此，我们引入了容忍区域混淆矩阵（TOR-Confusion Matrix），通过建模容忍扰动引发的类别间转移概率来衡量分类器的脆弱性。我们在图像与表格数据集上验证了该方法，证明其能同时提供可解释性和鲁棒性评估能力。
原文摘要 · Abstract (English)
In this work, we propose a unified approach for diagnosing misclassification and assessing the robustness of black-box classifiers. Central to our method is an optimization framework that modifies an instance so that the classifier predicts a specified target label, while ensuring that the modification remains easily explainable. The objective function contains two components: an explainability-aware $L_0$ (XA-$L_0$) penalty that promotes sparse and interpretable modifications, and a classifier loss objective that steers the perturbed instance toward the desired output. This integrated optimization formulation is used both to identify the underlying causes of misclassification and to evaluate robustness by determining how an instance can change within a tolerance region before being reassigned to another class. To quantify robustness, we introduce the Tolerance Region Confusion Matrix (TOR-Confusion Matrix), which measures a classifier's susceptibility by modeling the class-to-class transition probabilities induced by tolerance-bounded perturbations. We validate the proposed method on both image and tabular datasets, demonstrating its ability to jointly deliver interpretability and robustness assessment.
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