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Summary:
The convergence of 6G connectivity artificial intelligence and smart healthcare infrastructure is transforming medical imaging workflows. In 6G-enabled smart hospitals radiology images such as CT and MRI scans are processed and transmitted at ultra-low latency between edge devices cloud platforms and remote specialists. However this speed and openness introduce critical security vulnerabilities notably the risk of deepfake-based manipulation. Malicious actors can exploit generative adversarial networks (GANs) to inject erase or alter anomalies in medical scans—potentially leading to misdiagnoses insurance fraud or patient endangerment. In this paper we introduce a federated deepfake detection framework tailored to 6G healthcare networks. Our approach uses a DenseNet-based convolutional neural network collaboratively trained across multiple medical institutions ensuring privacy preservation through decentralised learning. We simulate edge-based federated training scenarios on benchmark datasets including real and manipulated CT images and evaluate the model’s robustness in both intra- and cross-dataset conditions. The results show strong generalisation capabilitiesAuthor(s):
Romain Verdy-Ricard
ESME, France
France
Emma Perales
ESME, France
France
Mohamed-Aymen Labiod
University of Paris-Est Creteil, LISSI, TincNET (CIR), F-94400, Vitry-sur-Seine, France
France
Gueltoum Bendiab
Department of Electronics, University of Frères Mentouri, Constantine 25000, Algeria
Algeria
Yasmina Chenoune
ESME
France