A Comprehensive Review and Analysis of Deep Learning-Based Medical Image Adversarial Attack and Defense

Gladys W. Muoka, Ding Yi (), Chiagoziem C. Ukwuoma, Albert Mutale, Chukwuebuka J. Ejiyi, Asha Khamis Mzee, Emmanuel S. A. Gyarteng, Ali Alqahtani and Mugahed A. Al-antari ()
Additional contact information
Gladys W. Muoka: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Ding Yi: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Chiagoziem C. Ukwuoma: College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu 610059, China
Albert Mutale: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Chukwuebuka J. Ejiyi: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Asha Khamis Mzee: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Emmanuel S. A. Gyarteng: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Ali Alqahtani: Center for Artificial Intelligence and Computer Science Department, King Khalid University, Abha 61421, Saudi Arabia
Mugahed A. Al-antari: Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2023, vol. 11, issue 20, 1-41

Abstract: Deep learning approaches have demonstrated great achievements in the field of computer-aided medical image analysis, improving the precision of diagnosis across a range of medical disorders. These developments have not, however, been immune to the appearance of adversarial attacks, creating the possibility of incorrect diagnosis with substantial clinical implications. Concurrently, the field has seen notable advancements in defending against such targeted adversary intrusions in deep medical diagnostic systems. In the context of medical image analysis, this article provides a comprehensive survey of current advancements in adversarial attacks and their accompanying defensive strategies. In addition, a comprehensive conceptual analysis is presented, including several adversarial attacks and defensive strategies designed for the interpretation of medical images. This survey, which draws on qualitative and quantitative findings, concludes with a thorough discussion of the problems with adversarial attack and defensive mechanisms that are unique to medical image analysis systems, opening up new directions for future research. We identified that the main problems with adversarial attack and defense in medical imaging include dataset and labeling, computational resources, robustness against target attacks, evaluation of transferability and adaptability, interpretability and explainability, real-time detection and response, and adversarial attacks in multi-modal fusion. The area of medical imaging adversarial attack and defensive mechanisms might move toward more secure, dependable, and therapeutically useful deep learning systems by filling in these research gaps and following these future objectives.

Keywords: medical image analysis; deep learning; adversary attack; adversarial defense; deep neural networks (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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