Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations

• URL: http://arxiv.org/abs/2603.15867v1
• Date: Mon, 16 Mar 2026 19:56:57 GMT
• Title: Evaluating Black-Box Vulnerabilities with Wasserstein-Constrained Data Perturbations
• Abstract summary: We apply Optimal Transport theory in the analysis of responses of Machine Learning models to variations in the distribution of input variables.<n>We find the closest distribution, in the Wasserstein sense, that satisfies a given constraintt and examine its impact on model behavior.
• Score: 2.9220076568786335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The massive use of Machine Learning (ML) tools in industry comes with critical challenges, such as the lack of explainable models and the use of black-box algorithms. We address this issue by applying Optimal Transport theory in the analysis of responses of ML models to variations in the distribution of input variables. We find the closest distribution, in the Wasserstein sense, that satisfies a given constraintt and examine its impact on model behavior. Furthermore, we establish convergence results for this projected distribution and demonstrate our approach using examples and real-world datasets in both regression and classification settings.

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