  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
imperceptible but yet precise perturbations. 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 dataset 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 at the following link
\url{https://iplab.dmi.unict.it/MITS-GAN-2024/}.
