Adversarial examples undermine the robustness of deep neural networks (DNNs), posing a major challenge to deep learning. Although existing research has focused on external representations, such as shifts in feature distributions, internal dependencies between features, essential for model decisions, are often overlooked. Adversarial examples disrupt these dependencies, but quantifying such changes in high-dimensional feature spaces remains computationally challenging and underexplored. To address these challenges, we propose the Copula-based Adversarial Feature Index (CAFI), a novel metric that quantifies feature dependencies in the copula space. Copulas enable explicit modeling of feature interdependencies, overcoming limitations of methods that treat features as independent. By leveraging the degrees of freedom (dofs) in copula space, CAFI quantifies how adversarial perturbations destabilize these feature dependencies. Robust models maintain stable dependencies under attack, while non-robust models exhibit significant disruption. Beyond its diagnostic utility, CAFI also serves as a regularization tool to enhance adversarial training. Experimental results in multiple datasets show that incorporating CAFI regularization improves robustness by up to 8.61% against strong attacks like FWA, while preserving competitive accuracy on benign examples. The code is available at https://github.com/lynneliu-creator/CAFI.