MITS-GAN: Safeguarding medical imaging from tampering with generative adversarial networks.

Autor: Pasqualino G; Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, Catania, 95126, Italy., Guarnera L; Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, Catania, 95126, Italy., Ortis A; Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, Catania, 95126, Italy. Electronic address: alessandro.ortis@unict.it., Battiato S; Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, Catania, 95126, Italy.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Dec; Vol. 183, pp. 109248. Date of Electronic Publication: 2024 Oct 09.
DOI: 10.1016/j.compbiomed.2024.109248
Abstrakt: The progress in generative models, particularly Generative Adversarial Networks (GANs), opened new possibilities for image generation but raised concerns about potential malicious uses, especially in sensitive areas like medical imaging. This study introduces MITS-GAN, a novel approach to prevent tampering in medical images, with a specific focus on CT scans. The approach disrupts the output of the attacker's CT-GAN architecture by introducing finely tuned perturbations that are imperceptible to the human eye. Specifically, the proposed approach involves the introduction of appropriate Gaussian noise to the input as a protective measure against various attacks. Our method aims to enhance tamper resistance, comparing favorably to existing techniques. Experimental results on a CT scan demonstrate MITS-GAN's superior performance, emphasizing its ability to generate tamper-resistant images with negligible artifacts. As image tampering in medical domains poses life-threatening risks, our proactive approach contributes to the responsible and ethical use of generative models. This work provides a foundation for future research in countering cyber threats in medical imaging. Models and codes are publicly available. 1 .
Competing Interests: Declaration of competing interest None. All authors affirm that there are no interests to declare.
(Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: