Sparse Counterfactual Path Optimization for Efficient Model Robustness Evaluation

In this paper, we introduce Sparse Counterfactual Path Optimization (SCPO), a novel approach aimed at efficiently evaluating the robustness of machine learning models. As machine learning systems are increasingly deployed in critical applications, understanding their vulnerabilities to adversarial attacks is paramount. Existing adversarial example generation techniques often rely on discrete perturbations that fail to capture the complexities of real-world scenarios. In contrast, SCPO formulates a continuous trajectory in the input space, termed a counterfactual path, which induces minimal perturbations to trigger output changes in the model. By leveraging variational calculus and sparse optimization, our framework optimizes for the sparsest path, offering a realistic portrayal of how perturbations may unfold. This paper details the mathematical foundations of the problem, experiments across various domains, and demonstrates the superior interpretability and efficacy of SCPO compared to traditional adversarial methods. Our empirical evaluations show that SCPO not only minimizes the extent of perturbations required for output flipping but also enhances the understanding of model vulnerabilities, establishing a new benchmark for robustness assessment in machine learning.