Deepfake Detection in Medical Images

Deepfake Detection in Medical Images/span>

Authors- Professor Mr. R.A.Ghadage, Vrushali Anil Zinj, Priya Prashant Sarode

Abstract-The challenge of deepfake detection in medical imaging by leveraging the Mask R-CNN algorithm. Deepfakes, generated using advanced AI, manipulate images and videos, posing significant threats across industries, including healthcare. Altered medical images can lead to misdiagnoses, treatment delays, or inappropriate interventions, putting patients at risk. The ability to identify such manipulations is critical for maintaining trust in medical diagnoses. Hospitals relying on compromised data may experience disruptions, financial losses, and legal complications. This project aims to develop an efficient deep learning-based system to detect these synthetic alterations. By using the Mask R-CNN framework, the proposed solution seeks to accurately locate and flag tampered regions within medical images. The model enhances patient safety by ensuring the reliability of diagnostic data. Ultimately, this approach offers a safeguard for healthcare institutions against the dangers posed by deepfake technology.

DOI: /10.61463/ijset.vol.12.issue5.276