Securing Medical Imaging: A Comprehensive Review and Prospective Paths for Image Authentication

Abstract

Abstract: As medical image authentication systems depend more and more on cutting-edge technologies, the integrity and dependability of diagnostic procedures are seriously threatened by adversarial attacks. This abstract reviews the latest advancements in Deep Learning (DL)-specific image forgery detection techniques, highlighting common splicing and copy-move attacks as well. This survey covers a wide range of methods used in medical image forgery detection and highlights the role that sophisticated models play in maintaining the accuracy of medical images. The study looked at a range of deep learning and machine learning models as well as methods for isolating photos and using Generative Adversarial Networks (GANs) to detect tampering. The results showed that it is possible to reliably recognize intentionally created anomalies in medical imaging. The case studies show that deep learning performs exceptionally well in correctly identifying scans with injected tumours, especially when it comes to the localization of the region of interest. This works well in situations where localization is not feasible, as reduced-negativespace scans show.