MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks
It addresses life-threatening risks from image tampering in medical domains, offering a proactive solution for ethical AI use, though it appears incremental as it builds on existing GAN-based defense methods.
This study tackles the problem of preventing tampering in medical images, specifically CT scans, by introducing MITS-GAN, which uses finely tuned perturbations to disrupt attacker models, resulting in superior tamper resistance with negligible artifacts compared to existing techniques.
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 on https://iplab.dmi.unict.it/MITS-GAN-2024/.