Computers in Biology and Medicine
I am proud to announce that our work, MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks, has been published in Computers in Biology and Medicine (Q1, I.F. 7.0). This work addresses a critical problem in medical imaging: preventing the tampering of CT scans.
In this study, we present MITS-GAN, a novel approach based on generative adversarial networks designed to protect medical images from unauthorized modification. Our method introduces an imperceptible noise into the images, making it difficult for attackers to alter them undetected. We have demonstrated that MITS-GAN outperforms existing solutions by safeguarding image integrity.
This approach ensures the reliability of medical scans, which is crucial in preventing misdiagnosis and medical fraud. We believe this progress will open the way for a safer and more ethical use of AI in healthcare. The models and codes are publicly available for the research community!
ArXiv preprint: https://arxiv.org/abs/2401.09624