Abstract: Highlights•MI-Protector enhances adversarial robustness via example, model, and dual-level defenses.•DGMP integrates TLGAN and purifier for adversarial defense by generating purified images.•UMAN adds non-learnable and learnable UMANs at input and feature layers for defense.•MMIFAN uses model-level defense via attention noise injection at feature layers.•Experiments on 14 medical datasets verify our approach against adversarial attacks.
External IDs:doi:10.1016/j.inffus.2025.103822
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