Adversarial attacks in radiology – A systematic review

Vera Sorin, Shelly Soffer, Benjamin S. Glicksberg, Yiftach Barash, Eli Konen, Eyal Klang

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations

Abstract

Purpose: The growing application of deep learning in radiology has raised concerns about cybersecurity, particularly in relation to adversarial attacks. This study aims to systematically review the literature on adversarial attacks in radiology. Methods: We searched for studies on adversarial attacks in radiology published up to April 2023, using MEDLINE and Google Scholar databases. Results: A total of 22 studies published between March 2018 and April 2023 were included, primarily focused on image classification algorithms. Fourteen studies evaluated white-box attacks, three assessed black-box attacks and five investigated both. Eleven of the 22 studies targeted chest X-ray classification algorithms, while others involved chest CT (6/22), brain MRI (4/22), mammography (2/22), abdominal CT (1/22), hepatic US (1/22), and thyroid US (1/22). Some attacks proved highly effective, reducing the AUC of algorithm performance to 0 and achieving success rates up to 100 %. Conclusions: Adversarial attacks are a growing concern. Although currently the threats are more theoretical than practical, they still represent a potential risk. It is important to be alert to such attacks, reinforce cybersecurity measures, and influence the formulation of ethical and legal guidelines. This will ensure the safe use of deep learning technology in medicine.

Original languageEnglish
Article number111085
JournalEuropean Journal of Radiology
Volume167
DOIs
StatePublished - Oct 2023

Keywords

  • Adversarial attacks
  • Cybersecurity
  • Deep learning
  • Radiology

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