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arXiv · 2603.15867

Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations

Adriana Laurindo Monteiro·Jean-Michel Loubes
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Abstract

The growing use of Machine Learning (ML) tools comes with critical challenges, such as limited model explainability. We propose a global explainability framework that leverages Optimal Transport and Distributionally Robust Optimization to analyze how ML algorithms respond to constrained data perturbations. Our approach enforces constraints on feature-level statistics (e.g., brightness, age distribution), generating realistic perturbations that preserve semantic structure. We provide a model-agnostic diagnostic bench that applies to both tabular and image domains with solid theoretical guarantees. We validate the approach on real-world datasets providing interpretable robustness diagnostics that complement standard evaluation and fairness auditing tools.

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BibTeXRIS

Adriana Laurindo Monteiro, Jean-Michel Loubes. 2026-03-16. Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations. https://arxiv.org/abs/2603.15867

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