Fisher Geometry for Structured Confidence Adversarial Training

A new approach to adversarial training uses Fisher geometry and a hybrid cross-entropy/KL divergence objective to prevent pseudo-attacks. It generates perturbations that maximize distribution distance on the confidence manifold to improve model robustness.