Cutpurse: Decomposing Explanation-Guided Model Extraction on Tabular Data

Summary

Researchers examined whether explanation APIs for tabular classifiers help model extraction, separating attribution rankings from the bin-edge coordinates they supply. They found attribution gives no systematic advantage, and default Lime bin edges are quantiles of the explainer's background data rather than target-model properties. Their attack, Cutpurse, improves fidelity by 16.3 percentage points when bin edges are withheld, exceeding the undefended published attack by 7.0 points.

Topics

• Adversarial machine learning

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