Enhancing Adversarial Defense with "Immunity" in Mixture-of-Experts Networks

The author proposes the "Immunity" method to enhance adversarial robustness in Deep Neural Networks by utilizing a modified Mixture-of-Experts architecture and innovative loss functions based on Mutual Information and Position Stability. The core argument revolves around leveraging ensemble diversity, interpretability, and regularization techniques to improve model robustness against various adversarial attacks.