Model Reconstruction Using Counterfactual Explanations: Mitigating the Decision Boundary Shift

arXiv:2405.05369v1 Announce Type: new
Abstract: Counterfactual explanations find ways of achieving a favorable model outcome with minimum input perturbation. However, counterfactual explanations can also be exploited to steal the model by strategically training a surrogate model to give similar predictions as the original (target) model. In this work, we investigate model extraction by specifically leveraging the fact that the counterfactual explanations also lie quite close to the decision boundary. We propose a novel strategy for model extraction that we call …

abstract arxiv counterfactual cs.cr cs.lg decision however minimum predictions shift stat.ml training type work