Training-Free Adversarial Robustness in Computational MRI
This work proposes a novel, retraining-free defense against small adversarial perturbations in sub-sampled MRI reconstruction. It uses 'cyclic measurement consistency' to create an objective function that finds a small corrective perturbation to restore cycle consistency, effectively mitigating attacks without needing any network retraining.