Optimized Instance Alteration for Explaining and Assessing Robustness of Classifiers

• URL: http://arxiv.org/abs/2607.06637v1
• Date: Tue, 07 Jul 2026 14:25:34 GMT
• Title: Optimized Instance Alteration for Explaining and Assessing Robustness of Classifiers
• Abstract summary: We propose a unified approach for diagnosing misclassification and assessing the robustness of black-box classifiers.<n>Central to our method is an optimization framework that modifies an instance so that the classifier predicts a specified target label.<n>To quantify robustness, we introduce the Tolerance Region Confusion Matrix.
• Score: 8.221155505820198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information [19.50321703079894]
  We present a novel framework to uncover the weakness of the classifier via counterfactual examples.<n>We test the performance of our prober's misclassification detection and verify its effectiveness on the image classification benchmark datasets.
  arXiv  Detail & Related papers  (2025-03-12T05:05:58Z)
• Boosting Imperceptibility of Stable Diffusion-based Adversarial Examples Generation with Momentum [13.305800254250789]
  We propose a novel framework, Stable Diffusion-based Momentum Integrated Adversarial Examples (SD-MIAE) It generates adversarial examples that can effectively mislead neural network classifiers while maintaining visual imperceptibility and preserving the semantic similarity to the original class label. Experimental results demonstrate that SD-MIAE achieves a high misclassification rate of 79%, improving by 35% over the state-of-the-art method.
  arXiv  Detail & Related papers  (2024-10-17T01:22:11Z)
• Variational Classification [51.2541371924591]
  We derive a variational objective to train the model, analogous to the evidence lower bound (ELBO) used to train variational auto-encoders. Treating inputs to the softmax layer as samples of a latent variable, our abstracted perspective reveals a potential inconsistency. We induce a chosen latent distribution, instead of the implicit assumption found in a standard softmax layer.
  arXiv  Detail & Related papers  (2023-05-17T17:47:19Z)
• On the rate of convergence of a classifier based on a Transformer encoder [55.41148606254641]
  The rate of convergence of the misclassification probability of the classifier towards the optimal misclassification probability is analyzed. It is shown that this classifier is able to circumvent the curse of dimensionality provided the aposteriori probability satisfies a suitable hierarchical composition model.
  arXiv  Detail & Related papers  (2021-11-29T14:58:29Z)
• Taming Adversarial Robustness via Abstaining [7.1975923901054575]
  We consider a binary classification problem where the observations can be perturbed by an adversary. We include an abstaining option, where the classifier abstains from taking a decision when it has low confidence about the prediction. We show that there exist a tradeoff between the two metrics regardless of what method is used to choose the abstaining region.
  arXiv  Detail & Related papers  (2021-04-06T07:36:48Z)
• Provably End-to-end Label-Noise Learning without Anchor Points [118.97592870124937]
  We propose an end-to-end framework for solving label-noise learning without anchor points. Our proposed framework can identify the transition matrix if the clean class-posterior probabilities are sufficiently scattered.
  arXiv  Detail & Related papers  (2021-02-04T03:59:37Z)
• Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification [137.9939571408506]
  We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-16T04:09:04Z)
• Reachable Sets of Classifiers and Regression Models: (Non-)Robustness Analysis and Robust Training [1.0878040851638]
  We analyze and enhance robustness properties of both classifiers and regression models. Specifically, we verify (non-)robustness, propose a robust training procedure, and show that our approach outperforms adversarial attacks. Second, we provide techniques to distinguish between reliable and non-reliable predictions for unlabeled inputs, to quantify the influence of each feature on a prediction, and compute a feature ranking.
  arXiv  Detail & Related papers  (2020-07-28T10:58:06Z)
• Consistency Regularization for Certified Robustness of Smoothed Classifiers [89.72878906950208]
  A recent technique of randomized smoothing has shown that the worst-case $ell$-robustness can be transformed into the average-case robustness. We found that the trade-off between accuracy and certified robustness of smoothed classifiers can be greatly controlled by simply regularizing the prediction consistency over noise.
  arXiv  Detail & Related papers  (2020-06-07T06:57:43Z)
• Contradictory Structure Learning for Semi-supervised Domain Adaptation [67.89665267469053]
  Current adversarial adaptation methods attempt to align the cross-domain features. Two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain. We propose a novel framework for semi-supervised domain adaptation by unifying the learning of opposite structures.
  arXiv  Detail & Related papers  (2020-02-06T22:58:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.