Adversarial Robustness of MR Image Reconstruction Under Realistic Perturbations

Morshuis, Jan Nikolas; Gatidis, Sergios; Hein, Matthias; Baumgartner, Christian F.

doi:10.1007/978-3-031-17247-2_3

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…The work in [49] aims to identify anomalies in the frequency domain and mitigate AA in medical images. Besides, we found that the surrogate models can reduce attack effects, and GANs strategies tend to be most applied in this context, such as [54], [59][60][61][62][63]. Figure 5(c) summarizes the number of studies that proposed defensive strategies, corresponding attacks, and defenses.…”

Section: Objectivementioning

confidence: 97%

Security and Privacy in Machine Learning for Health Systems: Strategies and Challenges

de Aguiar,

Traina,

Traina

2023

Yearb Med Inform

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Objectives: Machine learning (ML) is a powerful asset to support physicians in decision-making procedures, providing timely answers. However, ML for health systems can suffer from security attacks and privacy violations. This paper investigates studies of security and privacy in ML for health. Methods: We examine attacks, defenses, and privacy-preserving strategies, discussing their challenges. We conducted the following research protocol: starting a manual search, defining the search string, removing duplicated papers, filtering papers by title and abstract, then their full texts, and analyzing their contributions, including strategies and challenges. Finally, we collected and discussed 40 papers on attacks, defense, and privacy. Results: Our findings identified the most employed strategies for each domain. We found trends in attacks, including universal adversarial perturbation (UAPs), generative adversarial network (GAN)-based attacks, and DeepFakes to generate malicious examples. Trends in defense are adversarial training, GAN-based strategies, and out-of-distribution (OOD) to identify and mitigate adversarial examples (AE). We found privacy-preserving strategies such as federated learning (FL), differential privacy, and combinations of strategies to enhance the FL. Challenges in privacy comprehend the development of attacks that bypass fine-tuning, defenses to calibrate models to improve their robustness, and privacy methods to enhance the FL strategy. Conclusions: In conclusion, it is critical to explore security and privacy in ML for health, because it has grown risks and open vulnerabilities. Our study presents strategies and challenges to guide research to investigate issues about security and privacy in ML applied to health systems.

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Section: Objectivementioning

confidence: 97%

Security and Privacy in Machine Learning for Health Systems: Strategies and Challenges

de Aguiar,

Traina,

Traina

2023

Yearb Med Inform

View full text Add to dashboard Cite

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Robustness and exploration of variational and machine learning approaches to inverse problems: An overview

Auras,

Gandikota,

Droege

et al. 2024

GAMM-Mitteilungen

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This paper provides an overview of current approaches for solving inverse problems in imaging using variational methods and machine learning. A special focus lies on point estimators and their robustness against adversarial perturbations. In this context results of numerical experiments for a one‐dimensional toy problem are provided, showing the robustness of different approaches and empirically verifying theoretical guarantees. Another focus of this review is the exploration of the subspace of data‐consistent solutions through explicit guidance to satisfy specific semantic or textural properties.

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