Adversarial Surrogate

Adversarial surrogate methods aim to improve the robustness of machine learning models against adversarial attacks by minimizing a surrogate loss function that accounts for potential perturbations. Current research focuses on understanding the statistical consistency of these surrogate losses, particularly investigating conditions under which minimizing the surrogate risk guarantees minimization of the true adversarial risk, and analyzing the properties of resulting classifiers, such as uniqueness. This work is crucial for developing theoretically sound and practically effective adversarial training techniques, impacting the reliability and security of machine learning systems across various applications.