Stress-Testing ML Pipelines with Adversarial Data Corruption

• URL: http://arxiv.org/abs/2506.01230v1
• Date: Mon, 02 Jun 2025 00:41:24 GMT
• Title: Stress-Testing ML Pipelines with Adversarial Data Corruption
• Authors: Jiongli Zhu, Geyang Xu, Felipe Lorenzi, Boris Glavic, Babak Salimi,
• Abstract summary: Regulators now demand evidence that high-stakes systems can withstand realistic, interdependent errors.<n>We introduce SAVAGE, a framework that formally models data-quality issues through dependency graphs and flexible corruption templates.<n>Savanage employs a bi-level optimization approach to efficiently identify vulnerable data subpopulations and fine-tune corruption severity.
• Score: 11.91482648083998
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Structured data-quality issues, such as missing values correlated with demographics, culturally biased labels, or systemic selection biases, routinely degrade the reliability of machine-learning pipelines. Regulators now increasingly demand evidence that high-stakes systems can withstand these realistic, interdependent errors, yet current robustness evaluations typically use random or overly simplistic corruptions, leaving worst-case scenarios unexplored. We introduce SAVAGE, a causally inspired framework that (i) formally models realistic data-quality issues through dependency graphs and flexible corruption templates, and (ii) systematically discovers corruption patterns that maximally degrade a target performance metric. SAVAGE employs a bi-level optimization approach to efficiently identify vulnerable data subpopulations and fine-tune corruption severity, treating the full ML pipeline, including preprocessing and potentially non-differentiable models, as a black box. Extensive experiments across multiple datasets and ML tasks (data cleaning, fairness-aware learning, uncertainty quantification) demonstrate that even a small fraction (around 5 %) of structured corruptions identified by SAVAGE severely impacts model performance, far exceeding random or manually crafted errors, and invalidating core assumptions of existing techniques. Thus, SAVAGE provides a practical tool for rigorous pipeline stress-testing, a benchmark for evaluating robustness methods, and actionable guidance for designing more resilient data workflows.

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