Training-Free Adversarial Robustness in Computational MRI

Mahdi Saberi, Chi Zhang, Mehmet Akcakaya
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:106681-106710, 2026.

Abstract

Deep learning (DL) methods have become the state-of-the-art for reconstructing sub-sampled magnetic resonance imaging (MRI) data. However, studies have shown that these methods are susceptible to small adversarial input perturbations, resulting in major distortions in the output images. Various strategies have been proposed to reduce the effects of these attacks, but they require retraining. In this work, we propose a novel approach for mitigating adversarial attacks on MRI reconstruction models without any retraining. Based on the idea of cyclic measurement consistency, we devise a novel mitigation objective that is minimized in a small ball around the attack input. Results show that our method substantially reduces the impact of adversarial perturbations across different datasets, attack types/strengths and PD-DL networks, and qualitatively and quantitatively outperforms conventional mitigation methods. We also introduce a practically relevant scenario for small adversarial perturbations that models impulse noise in raw data, which relates to herringbone artifacts, and show the applicability of our approach in this setting. Finally, we show our mitigation approach remains effective in two realistic extension scenarios: a blind setup, where the attack strength or algorithm is not known to the user; and an adaptive attack setup, where the attacker has full knowledge of the defense strategy. Code available at: https://github.com/MahdiSaberii/CycMit-MRI

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-saberi26a, title = {Training-Free Adversarial Robustness in Computational {MRI}}, author = {Saberi, Mahdi and Zhang, Chi and Akcakaya, Mehmet}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {106681--106710}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/saberi26a/saberi26a.pdf}, url = {https://proceedings.mlr.press/v306/saberi26a.html}, abstract = {Deep learning (DL) methods have become the state-of-the-art for reconstructing sub-sampled magnetic resonance imaging (MRI) data. However, studies have shown that these methods are susceptible to small adversarial input perturbations, resulting in major distortions in the output images. Various strategies have been proposed to reduce the effects of these attacks, but they require retraining. In this work, we propose a novel approach for mitigating adversarial attacks on MRI reconstruction models without any retraining. Based on the idea of cyclic measurement consistency, we devise a novel mitigation objective that is minimized in a small ball around the attack input. Results show that our method substantially reduces the impact of adversarial perturbations across different datasets, attack types/strengths and PD-DL networks, and qualitatively and quantitatively outperforms conventional mitigation methods. We also introduce a practically relevant scenario for small adversarial perturbations that models impulse noise in raw data, which relates to herringbone artifacts, and show the applicability of our approach in this setting. Finally, we show our mitigation approach remains effective in two realistic extension scenarios: a blind setup, where the attack strength or algorithm is not known to the user; and an adaptive attack setup, where the attacker has full knowledge of the defense strategy. Code available at: https://github.com/MahdiSaberii/CycMit-MRI} }
Endnote
%0 Conference Paper %T Training-Free Adversarial Robustness in Computational MRI %A Mahdi Saberi %A Chi Zhang %A Mehmet Akcakaya %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-saberi26a %I PMLR %P 106681--106710 %U https://proceedings.mlr.press/v306/saberi26a.html %V 306 %X Deep learning (DL) methods have become the state-of-the-art for reconstructing sub-sampled magnetic resonance imaging (MRI) data. However, studies have shown that these methods are susceptible to small adversarial input perturbations, resulting in major distortions in the output images. Various strategies have been proposed to reduce the effects of these attacks, but they require retraining. In this work, we propose a novel approach for mitigating adversarial attacks on MRI reconstruction models without any retraining. Based on the idea of cyclic measurement consistency, we devise a novel mitigation objective that is minimized in a small ball around the attack input. Results show that our method substantially reduces the impact of adversarial perturbations across different datasets, attack types/strengths and PD-DL networks, and qualitatively and quantitatively outperforms conventional mitigation methods. We also introduce a practically relevant scenario for small adversarial perturbations that models impulse noise in raw data, which relates to herringbone artifacts, and show the applicability of our approach in this setting. Finally, we show our mitigation approach remains effective in two realistic extension scenarios: a blind setup, where the attack strength or algorithm is not known to the user; and an adaptive attack setup, where the attacker has full knowledge of the defense strategy. Code available at: https://github.com/MahdiSaberii/CycMit-MRI
APA
Saberi, M., Zhang, C. & Akcakaya, M.. (2026). Training-Free Adversarial Robustness in Computational MRI. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:106681-106710 Available from https://proceedings.mlr.press/v306/saberi26a.html.

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